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                    <h1 class="text-lg md:text-xl font-bold text-gray-800">arXiv 每日论文精选</h1>
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                        <i class="fa fa-calendar-o mr-1"></i>2025-10-14
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                    <span class="text-gray-500 mr-1"><i class="fa fa-file-text-o"></i> 总论文数:</span>
                    <span id="total-papers" class="font-semibold text-primary">199</span>
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                    <span class="text-gray-500 mr-1"><i class="fa fa-star"></i> 精选论文数:</span>
                    <span id="selected-papers" class="font-semibold text-accent">20</span>
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                <span id="display-count" class="font-medium">显示 199 篇论文 (共 199 篇)</span>
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<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.11639v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>OneRec-Think：面向生成式推荐系统的文本内推理
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
        </span>
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            OneRec-Think: In-Text Reasoning for Generative Recommendation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zhanyu Liu, Shiyao Wang, Xingmei Wang, Rongzhou Zhang, Jiaxin Deng, Honghui Bao,...
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究生成式推荐系统缺乏显式可控推理能力的问题，核心思想是通过项目对齐、推理激活和推理增强三个组件，构建能够进行显式推理的统一推荐框架。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文直接针对LLM在推荐系统中的显式推理能力，提出了统一的对话-推理-推荐框架，完全符合直接LLM应用和核心领域进展的关注点。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 17:20:13
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11639v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11639v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.IR</span></div>
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            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
                     查看完整摘要 <i class="fa fa-chevron-down ml-1 text-xs"></i> 
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    The powerful generative capacity of Large Language Models (LLMs) has instigated a paradigm shift in recommendation. However, existing generative models (e.g., OneRec) operate as implicit predictors, critically lacking the capacity for explicit and controllable reasoning-a key advantage of LLMs. To bridge this gap, we propose OneRec-Think, a unified framework that seamlessly integrates dialogue, reasoning, and personalized recommendation. OneRec-Think incorporates: (1) Itemic Alignment: cross-modal Item-Textual Alignment for semantic grounding; (2) Reasoning Activation: Reasoning Scaffolding to activate LLM reasoning within the recommendation context; and (3) Reasoning Enhancement, where we design a recommendation-specific reward function that accounts for the multi-validity nature of user preferences. Experiments across public benchmarks show state-of-the-art performance. Moreover, our proposed "Think-Ahead" architecture enables effective industrial deployment on Kuaishou, achieving a 0.159\% gain in APP Stay Time and validating the practical efficacy of the model's explicit reasoning capability.
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<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.11592v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>REGENT：面向实体感知多向量神经重排的相关性引导注意力机制
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
        </span>
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            REGENT: Relevance-Guided Attention for Entity-Aware Multi-Vector Neural Re-Ranking
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Shubham Chatterjee
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">研究神经重排模型在复杂信息需求和长文档场景下的内容选择问题。核心方法是将实体作为语义骨架，通过相关性引导注意力机制结合细粒度词汇匹配和高层语义推理，实现人类化的理解方式。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文通过实体引导注意力机制解决神经重排中的内容选择问题，直接关联搜索和推荐系统中的核心挑战，方法创新且具有通用性。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 16:31:42
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11592v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11592v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.IR</span><span class="category-tag">cs.CL</span></div>
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            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
                     查看完整摘要 <i class="fa fa-chevron-down ml-1 text-xs"></i> 
                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Current neural re-rankers often struggle with complex information needs and long, content-rich documents. The fundamental issue is not computational--it is intelligent content selection: identifying what matters in lengthy, multi-faceted texts. While humans naturally anchor their understanding around key entities and concepts, neural models process text within rigid token windows, treating all interactions as equally important and missing critical semantic signals. We introduce REGENT, a neural re-ranking model that mimics human-like understanding by using entities as a "semantic skeleton" to guide attention. REGENT integrates relevance guidance directly into the attention mechanism, combining fine-grained lexical matching with high-level semantic reasoning. This relevance-guided attention enables the model to focus on conceptually important content while maintaining sensitivity to precise term matches. REGENT achieves new state-of-the-art performance in three challenging datasets, providing up to 108% improvement over BM25 and consistently outperforming strong baselines including ColBERT and RankVicuna. To our knowledge, this is the first work to successfully integrate entity semantics directly into neural attention, establishing a new paradigm for entity-aware information retrieval.
                </div>
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</div>
<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.11560v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>生成式AI时代下的网络搜索特性分析
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Characterizing Web Search in The Age of Generative AI
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Elisabeth Kirsten, Jost Grosse Perdekamp, Mihir Upadhyay, Krishna P. Gummadi, Mu...
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究生成式AI时代下搜索系统的核心变化问题，通过对比传统搜索与生成式搜索在信息来源、知识依赖和概念多样性等维度的差异，揭示生成式搜索带来的新特性和评估需求。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文直接研究生成式AI对搜索系统的影响，对比传统搜索与生成式搜索的核心差异，完全契合搜索领域核心进展和LLM应用的研究重点。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 16:04:03
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11560v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11560v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.IR</span><span class="category-tag">cs.AI</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
                     查看完整摘要 <i class="fa fa-chevron-down ml-1 text-xs"></i> 
                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    The advent of LLMs has given rise to a new type of web search: Generative search, where LLMs retrieve web pages related to a query and generate a single, coherent text as a response. This output modality stands in stark contrast to traditional web search, where results are returned as a ranked list of independent web pages. In this paper, we ask: Along what dimensions do generative search outputs differ from traditional web search? We compare Google, a traditional web search engine, with four generative search engines from two providers (Google and OpenAI) across queries from four domains. Our analysis reveals intriguing differences. Most generative search engines cover a wider range of sources compared to web search. Generative search engines vary in the degree to which they rely on internal knowledge contained within the model parameters v.s. external knowledge retrieved from the web. Generative search engines surface varying sets of concepts, creating new opportunities for enhancing search diversity and serendipity. Our results also highlight the need for revisiting evaluation criteria for web search in the age of Generative AI.
                </div>
            </details>
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</div>
<div class="paper-card p-4 expanded">
    <div class="flex justify-between items-start mb-2">
        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.11438v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>生成式搜索引擎的偏好及如何协同优化网络内容
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            What Generative Search Engines Like and How to Optimize Web Content Cooperatively
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yujiang Wu, Shanshan Zhong, Yubin Kim, Chenyan Xiong
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究如何优化网页内容以提升在生成式搜索引擎中的曝光度。核心方法是自动学习生成搜索引擎的内容偏好规则，并利用这些规则通过提示工程和强化学习来重写网页内容。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文直接针对搜索领域的LLM应用，提出自动学习生成搜索引擎偏好的框架，与LLM在搜索中的直接应用和优化高度相关。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 14:10:26
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11438v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11438v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.IR</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
                     查看完整摘要 <i class="fa fa-chevron-down ml-1 text-xs"></i> 
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    By employing large language models (LLMs) to retrieve documents and generate natural language responses, Generative Engines, such as Google AI overview and ChatGPT, provide significantly enhanced user experiences and have rapidly become the new form of search. Their rapid adoption also drives the needs of Generative Engine Optimization (GEO), as content providers are eager to gain more traction from them. In this paper, we introduce AutoGEO, a framework to automatically learn generative engine preferences when using retrieved contents for response generation, and rewrite web contents for more such traction. AutoGEO first prompts frontier LLMs to explain generative engine preferences and extract meaningful preference rules from these explanations. Then it uses preference rules as context engineering for AutoGEO$_\text{API}$, a prompt-based GEO system, and as rule-based rewards to train AutoGEO$_\text{Mini}$, a cost-effective GEO model. Experiments on the standard GEO-Bench and two newly constructed benchmarks using real user queries demonstrate the effectiveness of AutoGEO in enhancing content traction while preserving search utility. Analyses confirm the learned rules' robustness and abilities to capture unique preferences in variant domains, and AutoGEO systems' ability to embed them in content optimization. The code is released at https://github.com/cxcscmu/AutoGEO.
                </div>
            </details>
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<div class="paper-card p-4 expanded">
    <div class="flex justify-between items-start mb-2">
        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.11358v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>LLM特定效用：检索增强生成的新视角
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            LLM-Specific Utility: A New Perspective for Retrieval-Augmented Generation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Hengran Zhang, Keping Bi, Jiafeng Guo, Jiaming Zhang, Shuaiqiang Wang, Dawei Yin...
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究RAG系统中检索段落对特定LLM的效用问题，核心思想是不同LLM对相同段落的效用存在差异，需要采用LLM特定的效用评估标准而非通用相关性。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出LLM特定效用的新概念，直接针对RAG系统中检索质量的核心问题，与LLM在搜索推荐领域的应用高度相关。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 12:57:45
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11358v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11358v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.IR</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
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                     查看完整摘要 <i class="fa fa-chevron-down ml-1 text-xs"></i> 
                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Retrieval-augmented generation (RAG) enhances large language models (LLMs) by incorporating external knowledge. While traditional retrieval focuses on relevance, RAG's effectiveness depends on the utility of retrieved passages, i.e., the usefulness in facilitating the generation of an accurate and comprehensive answer. Existing studies often treat utility as a generic attribute, ignoring the fact that different LLMs may benefit differently from the same passage due to variations in internal knowledge and comprehension ability. In this work, we introduce and systematically investigate the notion of LLM-specific utility. Through large-scale experiments across multiple datasets and LLMs, we demonstrate that human-annotated passages are not optimal for LLMs and that ground-truth utilitarian passages are not transferable across different LLMs. These findings highlight the necessity of adopting the LLM-specific utility in RAG research. Our findings indicate that some human-annotated passages are not ground-truth utilitarian passages for specific LLMs, partially due to the varying readability of queries and passages for LLMs, a tendency for which perplexity is a key metric. Based on these findings, we propose a benchmarking procedure for LLM-specific utility judgments. We evaluate existing utility judgment methods on six datasets and find that while verbalized methods using pseudo-answers perform robustly, LLMs struggle to assess utility effectively-failing to reject all passages for known queries and to select truly useful ones for unknown queries.
                </div>
            </details>
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</div>
<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.11317v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>下一个兴趣流：通过建模全域移动轨迹的推荐系统生成式预训练范式
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Next Interest Flow: A Generative Pre-training Paradigm for Recommender Systems by Modeling All-domain Movelines
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Chen Gao, Zixin Zhao, Lv Shao, Tong Liu
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究推荐系统中点击率预测的生成式建模问题，核心思想是通过预测表示用户未来意图的密集向量序列（Next Interest Flow），同时建模兴趣多样性和演化速度，并通过双向对齐策略解决生成与判别阶段的目标不匹配问题。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文直接针对推荐系统的生成式预训练范式，提出了解决现有LLM方法和ID生成局限性的新方法，与核心领域进展和直接LLM应用高度相关。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 12:13:17
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11317v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11317v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.IR</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
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                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Click-Through Rate (CTR) prediction, a cornerstone of modern recommender systems, has been dominated by discriminative models that react to past user behavior rather than proactively modeling user intent. Existing generative paradigms attempt to address this but suffer from critical limitations: Large Language Model (LLM) based methods create a semantic mismatch by forcing e-commerce signals into a linguistic space, while ID-based generation is constrained by item memorization and cold-start issues. To overcome these limitations, we propose a novel generative pre-training paradigm. Our model learns to predict the Next Interest Flow, a dense vector sequence representing a user's future intent, while simultaneously modeling its internal Interest Diversity and Interest Evolution Velocity to ensure the representation is both rich and coherent. However, this two-stage approach introduces a critical objective mismatch between the generative and discriminative stages. We resolve this via a bidirectional alignment strategy, which harmonizes the two stages through cross-stage weight initialization and a dynamic Semantic Alignment Module for fine-tuning. Additionally, we enhance the underlying discriminative model with a Temporal Sequential Pairwise (TSP) mechanism to better capture temporal causality. We present the All-domain Moveline Evolution Network (AMEN), a unified framework implementing our entire pipeline. Extensive offline experiments validate AMEN's superiority over strong baselines, and a large-scale online A/B test demonstrates its significant real-world impact, delivering substantial improvements in key business metrics.
                </div>
            </details>
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<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.11122v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>DyKnow-RAG：面向电商搜索相关性中噪声检索增强生成的动态知识利用强化框架
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            DyKnow-RAG: Dynamic Knowledge Utilization Reinforcement Framework for Noisy Retrieval-Augmented Generation in E-commerce Search Relevance
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Tingqiao Xu, Shaowei Yao, Chenhe Dong, Yiming Jin, Zerui Huang, Dan Ou, Haihong ...
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究电商搜索中如何动态利用噪声外部知识提升查询-商品相关性建模。核心方法是基于分组相对策略优化的强化学习框架，通过训练两个策略组（无外部上下文vs单检索块）和后验驱动的组间优势缩放，自适应学习何时信任检索知识或回退到参数化知识。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文直接针对电商搜索相关性中的动态知识利用问题，核心方法结合了强化学习和RAG框架，与推荐系统、搜索领域高度相关。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 08:08:59
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11122v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11122v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.IR</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
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                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Accurately modeling query-item relevance drives e-commerce ranking, yet long-tail, knowledge-heavy, and fast-evolving queries exceed parametric LLM coverage. External context (reviews, attribute encyclopedias, UGC) can help but is noisy, and single-pass latency and cost forbid any clean-then-summarize step. The model must, per query, judge relevance and decide whether to use, partially use, or ignore the context. DyKnow-RAG is a dynamic noisy-RAG framework built on Group Relative Policy Optimization. It trains two rollout groups (no external context vs a single retrieved chunk) and applies posterior-driven inter-group advantage scaling that adaptively reweights their contributions by the per-query correctness gap. This teaches when to trust retrieval versus fall back to parametric knowledge, without process labels, value networks, or extra inference passes, preserving single-pass, single-chunk deployment under production latency. Training combines: (1) supervised initialization with a structured rationale that explicitly records the context-usage decision; (2) an RL pool prioritized by SFT uncertainty to focus where context choice is most consequential; and (3) an optional lightweight DPO warm start to stabilize with-context calibration. Under a unified retrieval/index and fixed latency budget, DyKnow-RAG outperforms SFT, DPO, and vanilla GRPO in offline tests, and delivers consistent lifts on GSB, Query Goodrate, and Item Goodrate in Taobao A/B testing. It is deployed in Taobao's production relevance system, serving live traffic. To our knowledge, it is among the first single-pass RAG solutions for e-commerce relevance, turning noisy external signals into reliable gains without added online complexity.
                </div>
            </details>
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</div>
<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.11100v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>HoMer：通过建模序列和集合上下文处理异构性以进行点击率预测
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            HoMer: Addressing Heterogeneities by Modeling Sequential and Set-wise Contexts for CTR Prediction
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Shuwei Chen, Jiajun Cui, Zhengqi Xu, Fan Zhang, Jiangke Fan, Teng Zhang, Xingxin...
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究推荐系统中CTR预测面临的三种异构性问题。核心方法是提出统一的Transformer编码器-解码器架构，通过特征对齐、集合级预测和统一结构设计来同时解决特征、上下文和架构异构性。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文直接针对推荐系统的核心CTR预测问题，提出统一的Transformer架构处理异构特征和上下文，与关注领域高度契合。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 07:47:03
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11100v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11100v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.IR</span><span class="category-tag">cs.AI</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
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                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Click-through rate (CTR) prediction, which models behavior sequence and non-sequential features (e.g., user/item profiles or cross features) to infer user interest, underpins industrial recommender systems. However, most methods face three forms of heterogeneity that degrade predictive performance: (i) Feature Heterogeneity persists when limited sequence side features provide less granular interest representation compared to extensive non-sequential features, thereby impairing sequence modeling performance; (ii) Context Heterogeneity arises because a user's interest in an item will be influenced by other items, yet point-wise prediction neglects cross-item interaction context from the entire item set; (iii) Architecture Heterogeneity stems from the fragmented integration of specialized network modules, which compounds the model's effectiveness, efficiency and scalability in industrial deployments. To tackle the above limitations, we propose HoMer, a Homogeneous-Oriented TransforMer for modeling sequential and set-wise contexts. First, we align sequence side features with non-sequential features for accurate sequence modeling and fine-grained interest representation. Second, we shift the prediction paradigm from point-wise to set-wise, facilitating cross-item interaction in a highly parallel manner. Third, HoMer's unified encoder-decoder architecture achieves dual optimization through structural simplification and shared computation, ensuring computational efficiency while maintaining scalability with model size. Without arduous modification to the prediction pipeline, HoMer successfully scales up and outperforms our industrial baseline by 0.0099 in the AUC metric, and enhances online business metrics like CTR/RPM by 1.99%/2.46%. Additionally, HoMer saves 27% of GPU resources via preliminary engineering optimization, further validating its superiority and practicality.
                </div>
            </details>
    </div>
</div>
<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.11066v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>用于点击率预测中用户兴趣建模的解耦多模态融合
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Decoupled Multimodal Fusion for User Interest Modeling in Click-Through Rate Prediction
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Alin Fan, Hanqing Li, Sihan Lu, Jingsong Yuan, Jiandong Zhang
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究多模态推荐系统中内容语义与行为信号之间的细粒度交互问题，核心思想是通过目标感知特征桥接不同嵌入空间的语义鸿沟，并设计解耦注意力机制实现模态增强的用户兴趣建模。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文直接针对推荐系统中的多模态融合问题，提出解耦式融合方法增强用户兴趣建模，与核心领域进展和直接LLM应用高度相关。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 07:06:26
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11066v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11066v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.IR</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
                     查看完整摘要 <i class="fa fa-chevron-down ml-1 text-xs"></i> 
                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Modern industrial recommendation systems improve recommendation performance by integrating multimodal representations from pre-trained models into ID-based Click-Through Rate (CTR) prediction frameworks. However, existing approaches typically adopt modality-centric modeling strategies that process ID-based and multimodal embeddings independently, failing to capture fine-grained interactions between content semantics and behavioral signals. In this paper, we propose Decoupled Multimodal Fusion (DMF), which introduces a modality-enriched modeling strategy to enable fine-grained interactions between ID-based collaborative representations and multimodal representations for user interest modeling. Specifically, we construct target-aware features to bridge the semantic gap across different embedding spaces and leverage them as side information to enhance the effectiveness of user interest modeling. Furthermore, we design an inference-optimized attention mechanism that decouples the computation of target-aware features and ID-based embeddings before the attention layer, thereby alleviating the computational bottleneck introduced by incorporating target-aware features. To achieve comprehensive multimodal integration, DMF combines user interest representations learned under the modality-centric and modality-enriched modeling strategies. Offline experiments on public and industrial datasets demonstrate the effectiveness of DMF. Moreover, DMF has been deployed on the product recommendation system of the international e-commerce platform Lazada, achieving relative improvements of 5.30% in CTCVR and 7.43% in GMV with negligible computational overhead.
                </div>
            </details>
    </div>
</div>
<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.11056v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>从推理大语言模型到BERT：面向搜索相关性的两阶段蒸馏框架
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            From Reasoning LLMs to BERT: A Two-Stage Distillation Framework for Search Relevance
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Runze Xia, Yupeng Ji, Yuxi Zhou, Haodong Liu, Teng Zhang, Piji Li
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究电商搜索中查询-服务相关性预测的延迟限制问题，核心思想是通过领域适应的教师LLM生成推理路径，再通过对比推理自蒸馏将复杂决策机制迁移到轻量级学生模型中。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文直接针对搜索系统中的延迟限制问题，提出两阶段蒸馏框架将LLM推理能力迁移到轻量级模型，完美契合搜索领域和LLM应用的核心关注点。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 06:46:43
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11056v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11056v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.IR</span><span class="category-tag">cs.AI</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
                     查看完整摘要 <i class="fa fa-chevron-down ml-1 text-xs"></i> 
                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Query-service relevance prediction in e-commerce search systems faces strict latency requirements that prevent the direct application of Large Language Models (LLMs). To bridge this gap, we propose a two-stage reasoning distillation framework to transfer reasoning capabilities from a powerful teacher LLM to a lightweight, deployment-friendly student model. In the first stage, we address the limitations of general-purpose LLMs by constructing a domain-adapted teacher model. This is achieved through a three-step process: domain-adaptive pre-training to inject platform knowledge, supervised fine-tuning to elicit reasoning skills, and preference optimization with a multi-dimensional reward model to ensure the generation of reliable and preference-aligned reasoning paths. This teacher can then automatically annotate massive query-service pairs from search logs with both relevance labels and reasoning chains. In the second stage, to address the challenges of architectural heterogeneity in standard distillation, we introduce Contrastive Reasoning Self-Distillation (CRSD). By modeling the behavior of the same student model under "standard" and "reasoning-augmented" inputs as a teacher-student relationship, CRSD enables the lightweight model to internalize the teacher's complex decision-making mechanisms without needing the explicit reasoning path at inference. Offline evaluations and online A/B testing in the Meituan search advertising system demonstrate that our framework achieves significant improvements across multiple metrics, validating its effectiveness and practical value.
                </div>
            </details>
    </div>
</div>
<div class="paper-card p-4 expanded">
    <div class="flex justify-between items-start mb-2">
        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.10955v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>HatLLM：基于LLM的推荐系统中用于增强协同建模的分层注意力掩码机制
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            HatLLM: Hierarchical Attention Masking for Enhanced Collaborative Modeling in LLM-based Recommendation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yu Cui, Feng Liu, Jiawei Chen, Canghong Jin, Xingyu Lou, Changwang Zhang, Jun Wa...
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究LLM在序列推荐中难以有效建模用户历史交互间的协同信号问题，核心方法是提出分层注意力掩码策略：浅层屏蔽不同物品间注意力以理解物品内语义，深层屏蔽物品内注意力以强制捕捉跨物品相关性。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文直接针对LLM在推荐系统中的关键局限——协同信号建模，提出了创新的分层注意力掩码方法，完美契合核心领域进展和直接LLM应用两个重点方向。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 03:05:03
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.10955v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.10955v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.IR</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Recent years have witnessed a surge of research on leveraging large language models (LLMs) for sequential recommendation. LLMs have demonstrated remarkable potential in inferring users' nuanced preferences through fine-grained semantic reasoning. However, they also exhibit a notable limitation in effectively modeling collaborative signals, i.e., behavioral correlations inherent in users' historical interactions. Our empirical analysis further reveals that the attention mechanisms in LLMs tend to disproportionately focus on tokens within the same item, thereby impeding the capture of cross-item correlations. To address this limitation, we propose a novel hierarchical attention masking strategy for LLM-based recommendation, termed HatLLM. Specifically, in shallow layers, HatLLM masks attention between tokens from different items, facilitating intra-item semantic understanding; in contrast, in deep layers, HatLLM masks attention within items, thereby compelling the model to capture cross-item correlations. This progressive, layer-wise approach enables LLMs to jointly model both token-level and item-level dependencies. Extensive experiments on three real-world datasets demonstrate that HatLLM achieves significant performance gains (9.13% on average) over existing LLM-based methods.
                </div>
            </details>
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</div>
<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.11696v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>QeRL：超越效率——面向大语言模型的量化增强强化学习
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            QeRL: Beyond Efficiency -- Quantization-enhanced Reinforcement Learning for LLMs
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Wei Huang, Yi Ge, Shuai Yang, Yicheng Xiao, Huizi Mao, Yujun Lin, Hanrong Ye, Si...
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究LLM强化学习训练的资源效率问题，核心思想是通过NVFP4量化和LoRA结合加速RL训练，并利用量化噪声增加策略熵来增强探索，同时引入自适应量化噪声机制动态优化训练过程。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文将量化技术与强化学习结合，不仅提升效率还增强探索能力，直接适用于LLM训练优化，对推荐和搜索系统的模型训练具有重要价值。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 17:55:09
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11696v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11696v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.CL</span><span class="category-tag">cs.CV</span></div>
            </div>
            
            
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    We propose QeRL, a Quantization-enhanced Reinforcement Learning framework for large language models (LLMs). While RL is essential for LLMs' reasoning capabilities, it is resource-intensive, requiring substantial GPU memory and long rollout durations. QeRL addresses these issues by combining NVFP4 quantization with Low-Rank Adaptation (LoRA), accelerating rollout phase of RL while reducing memory overhead. Beyond efficiency, our findings show that quantization noise increases policy entropy, enhancing exploration, and enabling the discovery of better strategies during RL. To further optimize exploration, QeRL introduces an Adaptive Quantization Noise (AQN) mechanism, which dynamically adjusts noise during training. Experiments demonstrate that QeRL delivers over 1.5 times speedup in the rollout phase. Moreover, this is the first framework to enable RL training of a 32B LLM on a single H100 80GB GPU, while delivering overall speedups for RL training. It also achieves faster reward growth and higher final accuracy than 16-bit LoRA and QLoRA, while matching the performance of full-parameter fine-tuning on mathematical benchmarks such as GSM8K (90.8%) and MATH 500 (77.4%) in the 7B model. These results establish QeRL as an efficient and effective framework for RL training in LLMs.
                </div>
            </details>
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</div>
<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.11693v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>扩展以语言为中心的全模态表示学习
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Scaling Language-Centric Omnimodal Representation Learning
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Chenghao Xiao, Hou Pong Chan, Hao Zhang, Weiwen Xu, Mahani Aljunied, Yu Rong
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">研究多模态表示学习中MLLM方法优越性的根本原因；核心发现是生成式预训练在共享表示空间内隐式实现了跨模态对齐，使得对比学习仅需轻量微调，并提出生成能力与表示性能正相关的缩放定律。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文揭示了生成式预训练在多模态表示学习中的核心优势，并提出生成-表示缩放定律，这对推荐系统中多模态特征统一建模具有直接指导意义。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 17:53:52
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11693v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11693v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CV</span></div>
            </div>
            
            
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Recent multimodal embedding approaches leveraging multimodal large language models (MLLMs) fine-tuned with contrastive learning (CL) have shown promising results, yet the underlying reasons behind their superiority remain underexplored. This work argues that a crucial advantage of MLLM-based approaches stems from implicit cross-modal alignment achieved during generative pretraining, where the language decoder learns to exploit multimodal signals within a shared representation space for generating unimodal outputs. Through analysis of anisotropy and kernel similarity structure, we empirically confirm that latent alignment emerges within MLLM representations, allowing CL to serve as a lightweight refinement stage. Leveraging this insight, we propose a Language-Centric Omnimodal Embedding framework, termed LCO-Emb. Extensive experiments across diverse backbones and benchmarks demonstrate its effectiveness, achieving state-of-the-art performance across modalities. Furthermore, we identify a Generation-Representation Scaling Law (GRSL), showing that the representational capabilities gained through contrastive refinement scales positively with the MLLM's generative capabilities. This suggests that improving generative abilities evolves as an effective paradigm for enhancing representation quality. We provide a theoretical explanation of GRSL, which formally links the MLLM's generative quality to the upper bound on its representation performance, and validate it on a challenging, low-resource visual-document retrieval task, showing that continual generative pretraining before CL can further enhance the potential of a model's embedding capabilities. Codes, models, and resources are available at https://github.com/LCO-Embedding/LCO-Embedding.
                </div>
            </details>
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</div>
<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.11615v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>面向大语言模型的令牌自适应知识蒸馏
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            LLM-Oriented Token-Adaptive Knowledge Distillation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xurong Xie, Zhucun Xue, Jiafu Wu, Jian Li, Yabiao Wang, Xiaobin Hu, Yong Liu, Ji...
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究LLM知识蒸馏中静态策略与动态学习过程不匹配的问题，核心方法是提出基于令牌难度的自适应蒸馏框架，通过动态聚焦和反向温度缩放实现针对性知识传递。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出面向LLM的知识蒸馏新方法，通过动态调整蒸馏策略提升模型压缩效果，直接适用于推荐系统等领域的模型优化。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 16:55:07
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11615v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11615v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Knowledge distillation (KD) is a key technique for compressing large-scale language models (LLMs), yet prevailing logit-based methods typically employ static strategies that are misaligned with the dynamic learning process of student models. These methods typically treat all tokens indiscriminately and apply a single, fixed temperature, resulting in suboptimal knowledge transfer. To address these limitations, we propose LLM-Oriented Token-Adaptive Knowledge Distillation (AdaKD), a novel framework that adapts the distillation process to the real-time learning state of each token. AdaKD consists of two synergistic modules driven by a unified token difficulty metric. First, our Loss-Driven Adaptive Token Focusing (LATF) module dynamically adjusts the distillation focus by monitoring the student's learning stability, concentrating computational resources on the most valuable tokens at each training phase. Second, we introduce Inverse Difficulty Temperature Scaling (IDTS), a counterintuitive yet effective token-level temperature strategy. It employs low temperatures for difficult tokens for targeted error correction, and high temperatures for easy tokens to encourage students to learn from the teacher's complete and smooth output distribution, thereby enhancing generalization. As a plug-and-play framework, AdaKD can consistently improve the performance of various distillation methods on multiple model architectures and benchmarks.
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            </details>
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</div>
<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.11589v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>QDER：用于多向量文档重排的查询特定文档和实体表示
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
        </span>
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            QDER: Query-Specific Document and Entity Representations for Multi-Vector Document Re-Ranking
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Shubham Chatterjee, Jeff Dalton
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究多向量文档重排序的核心问题，核心方法是提出QDER模型，通过延迟聚合机制保持细粒度token和实体表示，将知识图谱语义集成到多向量模型中统一建模查询-文档关系。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文将知识图谱语义与多向量模型相结合，通过延迟聚合机制改进文档重排序，直接适用于搜索系统核心算法。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 16:31:06
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11589v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11589v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.IR</span><span class="category-tag">cs.CL</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Neural IR has advanced through two distinct paths: entity-oriented approaches leveraging knowledge graphs and multi-vector models capturing fine-grained semantics. We introduce QDER, a neural re-ranking model that unifies these approaches by integrating knowledge graph semantics into a multi-vector model. QDER's key innovation lies in its modeling of query-document relationships: rather than computing similarity scores on aggregated embeddings, we maintain individual token and entity representations throughout the ranking process, performing aggregation only at the final scoring stage - an approach we call "late aggregation." We first transform these fine-grained representations through learned attention patterns, then apply carefully chosen mathematical operations for precise matches. Experiments across five standard benchmarks show that QDER achieves significant performance gains, with improvements of 36% in nDCG@20 over the strongest baseline on TREC Robust 2004 and similar improvements on other datasets. QDER particularly excels on difficult queries, achieving an nDCG@20 of 0.70 where traditional approaches fail completely (nDCG@20 = 0.0), setting a foundation for future work in entity-aware retrieval.
                </div>
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<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.11483v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>检索增强推理的不确定性量化
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Uncertainty Quantification for Retrieval-Augmented Reasoning
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Heydar Soudani, Hamed Zamani, Faegheh Hasibi
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究检索增强推理系统中的不确定性量化问题，核心思想是通过对推理步骤施加扰动来改变检索器和生成器的输入，从而在迭代反馈循环中捕捉检索和生成过程的不确定性。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文直接针对检索增强推理系统的不确定性量化问题，其核心方法通过扰动多步推理过程来捕捉检索和生成的不确定性，对推荐系统和搜索中的可信AI应用具有直接价值。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 14:55:28
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11483v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11483v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.IR</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Retrieval-augmented reasoning (RAR) is a recent evolution of retrieval-augmented generation (RAG) that employs multiple reasoning steps for retrieval and generation. While effective for some complex queries, RAR remains vulnerable to errors and misleading outputs. Uncertainty quantification (UQ) offers methods to estimate the confidence of systems' outputs. These methods, however, often handle simple queries with no retrieval or single-step retrieval, without properly handling RAR setup. Accurate estimation of UQ for RAR requires accounting for all sources of uncertainty, including those arising from retrieval and generation. In this paper, we account for all these sources and introduce Retrieval-Augmented Reasoning Consistency (R2C)--a novel UQ method for RAR. The core idea of R2C is to perturb the multi-step reasoning process by applying various actions to reasoning steps. These perturbations alter the retriever's input, which shifts its output and consequently modifies the generator's input at the next step. Through this iterative feedback loop, the retriever and generator continuously reshape one another's inputs, enabling us to capture uncertainty arising from both components. Experiments on five popular RAR systems across diverse QA datasets show that R2C improves AUROC by over 5% on average compared to the state-of-the-art UQ baselines. Extrinsic evaluations using R2C as an external signal further confirm its effectiveness for two downstream tasks: in Abstention, it achieves ~5% gains in both F1Abstain and AccAbstain; in Model Selection, it improves the exact match by ~7% over single models and ~3% over selection methods.
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<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.11402v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>冷启动物品推荐中继承性流行度偏差研究
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            On Inherited Popularity Bias in Cold-Start Item Recommendation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Gregor Meehan, Johan Pauwels
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">研究冷启动推荐系统中流行度偏差的继承问题，核心发现是冷启动模型无法从交互数据推断流行度，转而基于内容特征估计流行度，导致对内容类似热门物品的冷物品过度预测。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文直接研究推荐系统中的冷启动问题，揭示了冷启动模型从热模型继承流行度偏差的机制，对推荐系统公平性和核心算法设计有重要启示。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 13:44:13
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11402v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11402v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.IR</span></div>
            </div>
            
            
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                 <summary class="text-sm text-primary cursor-pointer"> 
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Collaborative filtering (CF) recommender systems struggle with making predictions on unseen, or 'cold', items. Systems designed to address this challenge are often trained with supervision from warm CF models in order to leverage collaborative and content information from the available interaction data. However, since they learn to replicate the behavior of CF methods, cold-start models may therefore also learn to imitate their predictive biases. In this paper, we show that cold-start systems can inherit popularity bias, a common cause of recommender system unfairness arising when CF models overfit to more popular items, thereby maximizing user-oriented accuracy but neglecting rarer items. We demonstrate that cold-start recommenders not only mirror the popularity biases of warm models, but are in fact affected more severely: because they cannot infer popularity from interaction data, they instead attempt to estimate it based solely on content features. This leads to significant over-prediction of certain cold items with similar content to popular warm items, even if their ground truth popularity is very low. Through experiments on three multimedia datasets, we analyze the impact of this behavior on three generative cold-start methods. We then describe a simple post-processing bias mitigation method that, by using embedding magnitude as a proxy for predicted popularity, can produce more balanced recommendations with limited harm to user-oriented cold-start accuracy.
                </div>
            </details>
    </div>
</div>
<div class="paper-card p-4 expanded">
    <div class="flex justify-between items-start mb-2">
        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.10920v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>基于反事实推理的推荐系统对比解释
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Comparative Explanations via Counterfactual Reasoning in Recommendations
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yi Yu, Zhenxing Hu
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究推荐系统中反事实解释的事实准确性问题，核心思想是通过软交换操作生成反事实数据，为任意对比物品对提供可解释的推荐理由。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文直接针对推荐系统的可解释性核心问题，提出基于反事实推理的新方法，属于推荐系统领域的重要进展。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 02:31:03
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.10920v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.10920v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.IR</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Explainable recommendation through counterfactual reasoning seeks to identify the influential aspects of items in recommendations, which can then be used as explanations. However, state-of-the-art approaches, which aim to minimize changes in product aspects while reversing their recommended decisions according to an aggregated decision boundary score, often lead to factual inaccuracies in explanations. To solve this problem, in this work we propose a novel method of Comparative Counterfactual Explanations for Recommendation (CoCountER). CoCountER creates counterfactual data based on soft swap operations, enabling explanations for recommendations of arbitrary pairs of comparative items. Empirical experiments validate the effectiveness of our approach.
                </div>
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</div>
<div class="paper-card p-4 expanded">
    <div class="flex justify-between items-start mb-2">
        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.11620v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>通过多路径规划聚合增强长链思维推理
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
        </span>
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Enhancing Long Chain-of-Thought Reasoning through Multi-Path Plan Aggregation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Siheng Xiong, Ali Payani, Faramarz Fekri
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究长链思维推理中的规划错误问题，核心思想是通过多路径规划探索与聚合机制，结合过程级偏好优化，提升语言模型在长序列推理任务中的规划准确性。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出的多路径规划聚合框架和过程级优化方法，能够直接提升LLM在长序列推理任务中的表现，对推荐和搜索系统中的复杂推理场景具有重要应用价值。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 17:02:41
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11620v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11620v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
            </div>
            
            
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Inference-time scaling enhances the reasoning ability of a language model (LM) by extending its chain-of-thought (CoT). However, existing approaches typically generate the entire reasoning chain in a single forward pass, which often leads to CoT derailment, i.e., the reasoning trajectory drifting off course due to compounding errors. This problem is particularly severe for smaller LMs with long CoTs due to their limited capacity. To address this, we analyze raw long CoTs and uncover a reasoning hierarchy consisting of planning and execution steps. Our analysis reveals that most reasoning errors stem from incorrect planning. Motivated by this observation, we propose Multi-Path Plan Aggregation (MPPA), a framework that augments single-pass reasoning with plan exploration and aggregation. Following a variable interval schedule based on the token position, MPPA generates multiple candidate plans and aggregates them into a refined planning step. To maintain efficiency, we adopt a minimal design in which the base LM serves as the primary policy, while a lightweight LoRA module implements the plan aggregation policy. We further observe that outcome-reward RL is inefficient for long trajectories (e.g., exceeding 4K tokens). To overcome this, we introduce online Step-DPO, a process-level preference optimization scheme that leverages Twisted Sequential Monte Carlo (TSMC) to provide scalable stepwise supervision using small LMs. This yields more efficient training, improved stability, and higher accuracy. Extensive experiments on challenging math, science, and logical reasoning benchmarks demonstrate that, with only 10% SFT data and 5% of preference pairs, our method outperforms both the DeepSeek-R1 distillation baseline and the outcome-reward RL baseline across multiple base models and tasks.
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.11168v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>ELMO：通过低精度和峰值内存优化在大输出空间中实现效率
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>7/10
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        <div class="mb-2 text-base text-gray-700">
            ELMO: Efficiency via Low-precision and Peak Memory Optimization in Large Output Spaces
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jinbin Zhang, Nasib Ullah, Erik Schultheis, Rohit Babbar
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究极端多标签分类场景下模型训练的计算和内存效率问题，核心思想是采用纯低精度训练框架结合内存优化技术，在不依赖高精度主权重的情况下实现高效模型训练。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文针对大规模推荐系统中的极端多标签分类问题，提出了低精度训练和内存优化方法，直接解决推荐系统核心的计算效率瓶颈。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 08:59:13
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11168v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11168v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.CL</span><span class="category-tag">cs.IR</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Large output spaces, also referred to as Extreme multilabel classification (XMC), is a setting that arises, e.g., in large-scale tagging and product-to-product recommendation, and is characterized by the number of labels ranging from hundreds of thousands to millions. This means that the linear classification head, usually only a tiny fraction of the overall model, turns into the main driver for compute and memory demand. Current state-of-the-art XMC methods predominantly rely on FP16-FP32 mixed-precision training, which we show can be unstable, and inefficient in terms of memory usage and computational overhead. Meanwhile, existing low-precision methods typically retain higher precision for the classification layer. In this work, we propose ELMO, a pure low-precision training framework for XMC models using BFloat16 and Float8 data types. By leveraging Kahan summation and stochastic rounding, we demonstrate that XMC models can be effectively trained entirely in Float8, without relying on single-precision master weights or tensor scaling. Low-precision training, combined with our proposed memory optimizations -- gradient fusion and chunking -- enables significant reductions in GPU memory usage. For example, we train a 3-million-label XMC model with only 6.6 GiB of GPU memory, compared to the 39.7 GiB required by the optimized SOTA method, Renee without compromising accuracy.
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            <a href="https://www.alphaxiv.org/abs/2510.11602v1" target="_blank" rel="noopener noreferrer">
                解构注意力机制：探究高效语言建模的设计原则
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
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        <div class="mb-2 text-base text-gray-700">
            Deconstructing Attention: Investigating Design Principles for Effective Language Modeling
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Huiyin Xue, Nafise Sadat Moosavi, Nikolaos Aletras
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文直接研究注意力机制的设计原则，这属于'Enabling Transformer Tech'范畴，涉及Transformer架构的核心组件优化。改进的注意力机制设计可以直接应用于推荐系统和搜索中的序列建模、用户行为序列处理，以及广告中的上下文理解，提升模型效率和性能。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 16:42:14
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11602v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11602v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.LG</span></div>
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                    The success of Transformer language models is widely credited to their dot-product attention mechanism, which interweaves a set of key design principles: mixing information across positions (enabling multi-token interactions), sequence-dependent activations (where attention weights adapt to each input), a specific mathematical form (dot-product similarities plus softmax weighting), and coupling of queries and keys to evolving hidden states (grounding attention in the current layer). However, the necessity of each of these principles remains largely untested. In this work, we systematically deconstruct attention by designing controlled variants that selectively relax these principles, applied both uniformly across all layers and in hybrid architectures where only some layers retain standard attention. Our empirical analysis reveals that mechanisms for mixing tokens are indispensable, as their absence collapses models to near-random behavior, while the exact mathematical form and sequence dependency can be substantially relaxed, especially when preserved in just a subset of layers. Surprisingly, even variants that fail in isolation can achieve robust performance when interleaved with standard attention, highlighting a cooperative effect. These findings deepen our understanding of what truly underpins attention's effectiveness and open new avenues for simplifying language models without sacrificing performance.
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            <a href="https://www.alphaxiv.org/abs/2510.11598v1" target="_blank" rel="noopener noreferrer">
                MeTA-LoRA：面向大型语言模型的数据高效多任务微调
            </a>
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            <i class="fa fa-star mr-1"></i>8/10
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        <div class="mb-2 text-base text-gray-700">
            MeTA-LoRA: Data-Efficient Multi-Task Fine-Tuning for Large Language Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Bo Cheng, Xu Wang, Jinda Liu, Yi Chang, Yuan Wu
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文涉及LLM的高效微调技术（LoRA），属于'使能LLM技术'范畴。多任务微调和数据高效方法可直接应用于推荐系统、搜索和广告领域，在这些场景中通常需要同时优化多个目标（如点击率、转化率、停留时间），且标注数据有限。这种技术能够帮助构建更高效、更通用的排序模型，适应多目标优化需求。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 16:37:40
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11598v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11598v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    Low-Rank Adaptation (LoRA) has emerged as one of the most widely used parameter-efficient fine-tuning (PEFT) methods for adapting large language models (LLMs) to downstream tasks. While highly effective in single-task settings, it struggles to efficiently leverage inter-task knowledge in complex multi-task learning scenarios, often requiring substantial task-specific data to achieve optimal performance. To address this limitation, we introduce MeTA-LoRA, a two-stage optimization framework that significantly improves data efficiency in multi-task adaptation. In the first stage, task-specific LoRA adapters are learned using only a few samples from each involved dataset, enabling rapid adaptation without large-scale supervision. In the second stage, the shared LoRA adapter is updated by aggregating gradients from multiple tasks to promote knowledge transfer across tasks, further reducing data usage by leveraging common patterns. In both multi-task learning and multilingual learning scenarios, our method matches or surpasses the performance of traditional full-data LoRA fine-tuning approaches, while using significantly less task-specific data.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.11407v1" target="_blank" rel="noopener noreferrer">
                KnowRL：教导语言模型知道它们知道什么
            </a>
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        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
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        <div class="mb-2 text-base text-gray-700">
            KnowRL: Teaching Language Models to Know What They Know
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Sahil Kale, Devendra Singh Dhami
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">这篇论文涉及语言模型的知识边界识别和不确定性建模，这是LLM可信度和可靠性的核心问题。在推荐系统和搜索领域，准确识别模型的知识边界可以显著提升结果质量，避免推荐或检索超出模型能力范围的内容，从而提高用户体验和系统可靠性。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 13:47:14
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11407v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11407v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Truly reliable AI requires more than simply scaling up knowledge; it demands the ability to know what it knows and when it does not. Yet recent research shows that even the best LLMs misjudge their own competence in more than one in five cases, making any response born of such internal uncertainty impossible to fully trust. Inspired by self-improvement reinforcement learning techniques that require minimal data, we present a simple but powerful framework KnowRL that strengthens a model's internal understanding of its own feasibility boundaries, enabling safer and more responsible behaviour. Our framework combines two components: (i) introspection, where the model generates and classifies tasks it judges feasible or infeasible, and (ii) consensus-based rewarding, where stability of self-knowledge assessment is reinforced through internal agreement. By using internally generated data, this design strengthens consistency in self-knowledge and entirely avoids costly external supervision. In experiments on LLaMA-3.1-8B and Qwen-2.5-7B, KnowRL steadily improved self-knowledge, validated by both intrinsic self-consistency and extrinsic benchmarking. With nothing more than a small seed set and no external supervision, our method drove gains as high as 28% in accuracy and 12% in F1, outperforming baselines in just a few iterations. Our framework essentially unlocks the untapped capacity of LLMs to self-improve their knowledge awareness, opening the door to reliable, more accountable AI and safer deployment in critical applications. Owing to its simplicity and independence from external effort, we encourage applying this reliability-enhancing process to all future models.
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            <a href="https://www.alphaxiv.org/abs/2510.11278v1" target="_blank" rel="noopener noreferrer">
                ENIGMA：大型语言模型中推理与对齐的几何结构
            </a>
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        <div class="mb-2 text-base text-gray-700">
            ENIGMA: The Geometry of Reasoning and Alignment in Large-Language Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Gareth Seneque, Lap-Hang Ho, Nafise Erfanian Saeedi, Jeffrey Molendijk, Ariel Ku...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文探讨LLM中推理和对齐的几何结构，属于'使能LLM技术'范畴。理解LLM的几何表示和推理机制可以改进推荐和搜索系统中的语义理解、用户意图建模以及内容对齐，从而提升个性化推荐的准确性和搜索相关性。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 11:13:09
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11278v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11278v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span><span class="category-tag">68T50</span><span class="category-tag">I.2.7</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    We present Entropic Mutual-Information Geometry Large-Language Model Alignment (ENIGMA), a novel approach to Large-Language Model (LLM) training that jointly improves reasoning, alignment and robustness by treating an organisation's policies/principles as directions to move on a model's information manifold. Our single-loop trainer combines Group-Relative Policy Optimisation (GRPO), an on-policy, critic-free RL method with Chain-of-Thought (CoT)-format only rewards; a Self-Supervised Alignment with Mutual Information (SAMI)-style symmetric InfoNCE auxiliary; and an entropic Sinkhorn optimal-transport regulariser on hidden-state distributions to bound geometry drift. We also introduce infoNCE metrics that specialise to a standard MI lower bound under matched negatives to measure how strongly a model's CoT encodes these policies. These metrics include a Sufficiency Index (SI) that enables the selection and creation of principles that maximise downstream performance prior to training. In our experiments using small (1B) LLMs, high-SI principles predict steadier training dynamics and improved benchmark performance over GRPO ablations. Our information-geometry analysis of trained models validates desirable structural change in the manifold. These results support our hypothesis that reasoning, alignment, and robustness are projections of a single informationgeometric objective, and that models trained using ENIGMA demonstrate principled reasoning without the use of a reward model, offering a path to trusted capability
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            <a href="https://www.alphaxiv.org/abs/2510.11236v1" target="_blank" rel="noopener noreferrer">
                XQuant：通过跨层压缩实现超低位KV缓存量化
            </a>
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            XQuant: Achieving Ultra-Low Bit KV Cache Quantization with Cross-Layer Compression
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Haoqi Yang, Yao Yao, Zuchao Li, Baoyuan Qi, Guoming Liu, Hai Zhao
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于KV缓存量化技术，这直接属于'使能LLM技术'范畴，通过减少内存占用和提升推理效率来优化LLM。在推荐系统、搜索和广告中，更高效的LLM推理能够支持更大规模的部署、更快的响应速度和更低的运营成本，对于实时服务至关重要。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 10:17:21
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11236v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11236v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse natural language processing tasks. However, their extensive memory requirements, particularly due to KV cache growth during long-text understanding and generation, present significant challenges for deployment in resource-constrained environments. Quantization has emerged as a promising solution to reduce memory consumption while preserving historical information. We propose XQuant, a training-free and plug-and-play framework that achieves ultra-low equivalent bit-width KV cache quantization. XQuant introduces two key innovations: a computationally negligible data-free calibration method and cross-layer KV cache compression, enabling quantization to sub-1.4 bits. Extensive experiments on TruthfulQA and LongBench demonstrate that XQuant outperforms state-of-the-art methods (e.g., KIVI-2bit and AsymKV-1.5bit) by achieving lower bit-width while maintaining superior performance, establishing a better trade-off between memory efficiency and model accuracy.
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            <a href="https://www.alphaxiv.org/abs/2510.11170v1" target="_blank" rel="noopener noreferrer">
                EAGER：用于自适应推理时间缩放的熵感知生成
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            EAGER: Entropy-Aware GEneRation for Adaptive Inference-Time Scaling
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Daniel Scalena, Leonidas Zotos, Elisabetta Fersini, Malvina Nissim, Ahmet Üstün
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文关注推理时自适应缩放和熵感知生成，这属于'使能LLM技术'范畴，直接涉及LLM推理效率优化。在推荐系统、搜索和广告中，高效的推理时间缩放对于处理大规模用户请求、实现动态资源分配和降低服务成本具有重要应用价值，能够支撑实时个性化服务。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 09:04:28
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11170v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11170v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span></div>
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                    With the rise of reasoning language models and test-time scaling methods as a paradigm for improving model performance, substantial computation is often required to generate multiple candidate sequences from the same prompt. This enables exploration of different reasoning paths toward the correct solution, however, allocates the same compute budget for each prompt. Grounded on the assumption that different prompts carry different degrees of complexity, and thus different computation needs, we propose EAGer, a training-free generation method that leverages model uncertainty through token-wise entropy distribution to reduce redundant computation and concurrently improve overall performance. EAGer allows branching to multiple reasoning paths only in the presence of high-entropy tokens, and then reallocates the saved compute budget to the instances where exploration of alternative paths is most needed. We find that across multiple open-source models on complex reasoning benchmarks such as AIME 2025, EAGer can reallocate the budget without accessing target labels, achieving the best efficiency-performance trade-off in terms of reasoning length and Pass@k. When target labels are accessible, EAGer generates up to 65% fewer tokens (hence saving compute) and achieves up to 37% improvement in Pass@k compared to the Full Parallel Sampling.
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            <a href="https://www.alphaxiv.org/abs/2510.11104v1" target="_blank" rel="noopener noreferrer">
                通过非人类推理路径偏好优化增强大语言模型推理能力
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            Enhancing LLM Reasoning via Non-Human-Like Reasoning Path Preference Optimization
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Junjie Lu, Yuliang Liu, Chaofeng Qu, Wei Shen, Zhouhan Lin, Min Xu
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文属于'使能LLM技术'范畴，专注于提升LLM的推理能力。在搜索和推荐系统中，增强的推理能力可以直接应用于复杂查询理解、多步骤推理推荐、以及跨模态语义理解等场景，能够显著提升系统处理复杂用户意图和上下文依赖的能力。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 07:51:16
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11104v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11104v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                    Current approaches for strengthening LLM reasoning tend to introduce a training bias toward human-like reasoning trajectories. In step-wise preference optimization, in particular, dependence on human or higher-capacity model annotations for intermediate steps limits exploration of alternative, non-human-like reasoning paths and thus constrains achievable performance. Furthermore, through a small-scale pilot study, we observed that in approximately 75% of cases, the model's first erroneous step occurs after the lowest-confidence point. This suggests that guiding the model at its lowest-confidence point before an error provides more accurate supervision than locating the first explicit error. In this paper, we propose Confidence-Guided Reasoning Path Preference Optimization (CGPO), a method that leverages a confidence signal to identify points of maximal uncertainty in the model's reasoning process and applies self-generated, non-human-like reasoning-path guidance to mitigate trajectory drift. Our experiments span diverse models applied to both code and mathematical reasoning tasks. The results show that, with the same amount of training data, our method using data generated by a small model can achieve better performance in most cases compared with approaches using data generated by a strong model or human-annotated.
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            <a href="https://www.alphaxiv.org/abs/2510.11001v1" target="_blank" rel="noopener noreferrer">
                DND：通过动态嵌套深度提升大型语言模型
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            DND: Boosting Large Language Models with Dynamic Nested Depth
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Tieyuan Chen, Xiaodong Chen, Haoxing Chen, Zhenzhong Lan, Weiyao Lin, Jianguo Li
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出了一种提升LLM性能的新方法（动态嵌套深度），属于'使能LLM技术'范畴。这种效率提升技术可以直接应用于推荐系统和搜索中的LLM推理优化，通过更高效的架构实现更快的响应时间和更低的计算成本，对于大规模部署至关重要。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 04:22:57
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11001v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11001v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                    We introduce Dynamic Nested Depth (DND), a novel method that improves performance for off-the-shelf LLMs by selecting critical tokens to reprocess in a nested depth manner. Specifically, at the end of the given transformer layer, DND identifies more critical tokens with a router and feeds them back for an extra round of processing, effectively ``reviewing" difficult tokens while avoiding redundant computation for easier ones. The dynamic selection mechanism is tailored for precise control via two novel strategies: a router controlling loss to enhance token selection distinguishability, and a threshold control scheme to ensure selection stability. We demonstrate the effectiveness of DND by directly integrating it into pre-trained dense and MoE models during a post-training phase. On diverse benchmarks, this approach boosts the performances of the dense Qwen3-1.7B by 1.88% and the MoE Qwen3-30B-A3B by 0.87%, all with a minimal parameter and computing increase.
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            <a href="https://www.alphaxiv.org/abs/2510.10974v1" target="_blank" rel="noopener noreferrer">
                通过选择性关键令牌微调增强大型语言模型推理能力
            </a>
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        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
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        <div class="mb-2 text-base text-gray-700">
            Enhancing Large Language Model Reasoning via Selective Critical Token Fine-Tuning
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zhiwen Ruan, Yixia Li, He Zhu, Yun Chen, Peng Li, Yang Liu, Guanhua Chen
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文属于'使能LLM技术'范畴，专注于提升LLM推理能力，这是推荐、搜索和广告系统的核心需求。通过选择性令牌微调改进推理能力，可直接应用于提升推荐系统的逻辑推理、搜索查询理解以及广告相关性判断的准确性。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 03:25:36
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.10974v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.10974v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    Large language models (LLMs) primarily rely on supervised fine-tuning (SFT) as a key method to adapt pre-trained models to domain-specific tasks such as mathematical reasoning. However, standard SFT uniformly penalizes all tokens, neglecting that only a small subset of critical tokens determines reasoning correctness. This uniform supervision often causes reduced output diversity and limited generalization. We propose Critical Token Fine-tuning (CFT), a simple yet effective approach that updates only tokens identified as functionally indispensable via counterfactual perturbations. By focusing gradient signals on these decisive reasoning steps while preserving the diversity of non-critical tokens, CFT can enhance both generation and diversity. Extensive experiments on five models across three families (Qwen, OLMo, LLaMA) and eleven mathematical reasoning benchmarks show that CFT, despite fine-tuning on less than 12% of tokens, consistently outperforms standard SFT. Moreover, CFT enables test-time scaling through improved sampling diversity and provides a stronger initialization for reinforcement learning, sustaining performance gains in later training stages while maintaining higher entropy for better exploration. These results highlight CFT as a practical and general framework for efficient and robust LLM fine-tuning.
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            <a href="https://www.alphaxiv.org/abs/2510.10925v1" target="_blank" rel="noopener noreferrer">
                寻找你的最优教师：通过路由器引导的多教师蒸馏实现个性化数据合成
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
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        <div class="mb-2 text-base text-gray-700">
            Find Your Optimal Teacher: Personalized Data Synthesis via Router-Guided Multi-Teacher Distillation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Hengyuan Zhang, Shiping Yang, Xiao Liang, Chenming Shang, Yuxuan Jiang, Chaofan ...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文涉及多教师蒸馏和个性化数据合成，这是LLM蒸馏和模型压缩的核心技术。在推荐系统和搜索领域，这种技术可以用于将大型LLM蒸馏为更高效的个性化模型，用于实时推理或边缘部署，同时保持个性化性能。路由器引导的机制特别适合处理用户异质性，为不同用户群体选择最优的教师模型进行知识蒸馏。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 02:36:36
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.10925v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.10925v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Training student models on synthetic data generated by strong teacher models is a promising way to distilling the capabilities of teachers. However, recent studies show that stronger models are not always optimal teachers, revealing a mismatch between teacher outputs and student learnability. To address this issue, we propose PerSyn (Personalized data Synthesis), a novel synthesis strategy that operates under a new ``Route then Generate'' paradigm to create data tailored to each student model, enabling it to learn more effectively. Specifically, PerSyn first assigns each prompt to its optimal teacher via a query-level router that jointly considers student learnability and teacher response quality. Each teacher then synthesizes data only for its assigned prompts, making the process more efficient than the conventional ``Generate then Select'' paradigm, where all teachers must generate parallel responses for the entire prompt set before constructing the final dataset. Extensive experiments across different model families and scales demonstrate that PerSyn consistently achieves superior or comparable performance to all baselines in instruct tuning and math reasoning settings. Further analysis verifies the effectiveness of PerSyn and offers extra insights to propel future research.
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            <a href="https://www.alphaxiv.org/abs/2510.11190v1" target="_blank" rel="noopener noreferrer">
                FlexAC：迈向多模态大语言模型中关联推理的灵活控制
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            FlexAC: Towards Flexible Control of Associative Reasoning in Multimodal Large Language Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Shengming Yuan, Xinyu Lyu, Shuailong Wang, Beitao Chen, Jingkuan Song, Lianli Ga...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文聚焦于多模态大语言模型中的关联推理控制，属于'VLM Analogy for Heterogeneous Data'范畴。在推荐系统和搜索场景中，灵活控制跨模态关联推理对于理解用户意图、处理异构特征（如用户行为序列与上下文信息）至关重要，能够显著提升多模态推荐的准确性和可解释性。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 09:22:12
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11190v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11190v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Multimodal large language models (MLLMs) face an inherent trade-off between faithfulness and creativity, as different tasks require varying degrees of associative reasoning. However, existing methods lack the flexibility to modulate this reasoning strength, limiting MLLMs' adaptability across factual and creative scenarios. To bridge this gap, we propose equipping MLLMs with mechanisms that enable flexible control over associative reasoning. We begin by investigating the internal mechanisms underlying associative behavior in MLLMs and find that: (1) middle layers play a pivotal role in shaping model's associative tendencies, (2) modifying representations in these layers effectively regulates associative reasoning strength, and (3) hallucinations can be exploited to derive steering vectors that guide this modulation. Building on these findings, we introduce Flexible Association Control (FlexAC), a lightweight and training-free framework for modulating associative behavior in MLLMs. FlexAC first induces hallucination-guided intermediate representations to encode associative directions. Then, it selects high-association instances to construct effective associative steering vectors, whose strengths are adaptively calibrated to balance creative guidance with output stability. Finally, recognizing the multi-dimensional nature of associative reasoning, FlexAC incorporates task-specific associative vectors derived from a forward pass on a few target-domain samples, enabling models to follow diverse associative directions and better adapt to creative tasks. Notably, our method achieves up to a 5.8x improvement in creativity on Creation-MMBench and a 29% reduction in hallucination rate on CHAIR, surpassing existing baselines and demonstrating its effectiveness in enabling flexible control over associative reasoning in MLLMs. Our code is available at https://github.com/ylhz/FlexAC.
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            <a href="https://www.alphaxiv.org/abs/2510.11175v1" target="_blank" rel="noopener noreferrer">
                基于原型迭代构建的可靠跨模态对齐
            </a>
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            <i class="fa fa-star mr-1"></i>8/10
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        <div class="mb-2 text-base text-gray-700">
            Reliable Cross-modal Alignment via Prototype Iterative Construction
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xiang Ma, Litian Xu, Lexin Fang, Caiming Zhang, Lizhen Cui
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文涉及跨模态对齐技术，这与'VLM Analogy for Heterogeneous Data'焦点高度相关，其中可以将用户序列和上下文特征视为不同模态进行统一建模。原型迭代构建方法在搜索和推荐系统中具有直接应用潜力，可用于对齐用户行为、物品属性和上下文信息等多模态数据，提升跨模态检索和推荐性能。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 09:08:27
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11175v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11175v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Cross-modal alignment is an important multi-modal task, aiming to bridge the semantic gap between different modalities. The most reliable fundamention for achieving this objective lies in the semantic consistency between matched pairs. Conventional methods implicitly assume embeddings contain solely semantic information, ignoring the impact of non-semantic information during alignment, which inevitably leads to information bias or even loss. These non-semantic information primarily manifest as stylistic variations in the data, which we formally define as style information. An intuitive approach is to separate style from semantics, aligning only the semantic information. However, most existing methods distinguish them based on feature columns, which cannot represent the complex coupling relationship between semantic and style information. In this paper, we propose PICO, a novel framework for suppressing style interference during embedding interaction. Specifically, we quantify the probability of each feature column representing semantic information, and regard it as the weight during the embedding interaction. To ensure the reliability of the semantic probability, we propose a prototype iterative construction method. The key operation of this method is a performance feedback-based weighting function, and we have theoretically proven that the function can assign higher weight to prototypes that bring higher performance improvements. Extensive experiments on various benchmarks and model backbones demonstrate the superiority of PICO, outperforming state-of-the-art methods by 5.2\%-14.1\%.
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            <a href="https://www.alphaxiv.org/abs/2510.11297v1" target="_blank" rel="noopener noreferrer">
                大型语言模型是有效的知识图谱构建器吗？
            </a>
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            <i class="fa fa-star mr-1"></i>7/10
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            Are Large Language Models Effective Knowledge Graph Constructors?
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Ruirui Chen, Weifeng Jiang, Chengwei Qin, Bo Xiong, Fiona Liausvia, Dongkyu Choi...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文探讨LLMs在知识图谱构建中的应用，这直接关联到'直接LLM应用'领域，因为知识图谱是搜索和推荐系统的核心组件。构建高质量的知识图谱可以显著提升搜索相关性、推荐准确性和广告定向效果，属于LLM技术在RecSys/Search/Ads中的具体应用场景。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 11:37:48
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11297v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11297v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    Knowledge graphs (KGs) are vital for knowledge-intensive tasks and have shown promise in reducing hallucinations in large language models (LLMs). However, constructing high-quality KGs remains difficult, requiring accurate information extraction and structured representations that support interpretability and downstream utility. Existing LLM-based approaches often focus narrowly on entity and relation extraction, limiting coverage to sentence-level contexts or relying on predefined schemas. We propose a hierarchical extraction framework that organizes information at multiple levels, enabling the creation of semantically rich and well-structured KGs. Using state-of-the-art LLMs, we extract and construct knowledge graphs and evaluate them comprehensively from both structural and semantic perspectives. Our results highlight the strengths and shortcomings of current LLMs in KG construction and identify key challenges for future work. To advance research in this area, we also release a curated dataset of LLM-generated KGs derived from research papers on children's mental well-being. This resource aims to foster more transparent, reliable, and impactful applications in high-stakes domains such as healthcare.
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            <a href="https://www.alphaxiv.org/abs/2510.11217v1" target="_blank" rel="noopener noreferrer">
                面向RAG适配的领域特定数据生成框架
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            Domain-Specific Data Generation Framework for RAG Adaptation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Chris Xing Tian, Weihao Xie, Zhen Chen, Zhengyuan Yi, Hui Liu, Haoliang Li, Shiq...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文涉及检索增强生成(RAG)的数据生成框架，这属于直接LLM应用范畴，对搜索和推荐系统中的知识增强和个性化响应生成具有直接应用价值。通过领域特定数据生成，可以提升搜索和推荐系统中RAG组件的效果和适应性。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 09:59:49
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11217v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11217v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Retrieval-Augmented Generation (RAG) combines the language understanding and reasoning power of large language models (LLMs) with external retrieval to enable domain-grounded responses. Effectively adapting RAG systems to domain-specific settings requires specialized, context-rich training data beyond general-purpose question-answering. Here, we propose RAGen, a scalable and modular framework for generating domain-grounded question-answer-context (QAC) triples tailored to diverse RAG adaptation approaches. RAGen produces these QAC triples by identifying key concepts in documents, generating diverse questions guided by Bloom's Taxonomy-inspired principles, and pairing them with precise answers extracted from relevant contexts. RAGen supports multiple RAG adaptation strategies, including the optimization of key components such as the LLM, retriever, and embedding model, etc. Its modular pipeline features semantic chunking, hierarchical concept extraction, and multi-chunk retrieval, along with the introduction of curated distractor contexts to promote robust reasoning. Designed for scalability, RAGen efficiently handles large and evolving document corpora without redundant processing, making it especially suitable for dynamic evolving domains such as scientific research and enterprise knowledge bases.
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            <a href="https://www.alphaxiv.org/abs/2510.11052v1" target="_blank" rel="noopener noreferrer">
                潜在精炼解码：通过精炼信念状态增强基于扩散的语言模型
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        <span class="score-badge bg-green-100 text-green-800">
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        <div class="mb-2 text-base text-gray-700">
            Latent Refinement Decoding: Enhancing Diffusion-Based Language Models by Refining Belief States
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Qinglin Zhu, Yizhen Yao, Runcong Zhao, Yanzheng Xiang, Amrutha Saseendran, Chen ...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文属于'赋能LLM技术'范畴，聚焦扩散模型在语言生成中的改进。扩散模型作为新兴的生成范式，在推荐系统和搜索中具有潜在应用价值，可用于生成更高质量的推荐内容、搜索摘要或多样化广告文案。精炼信念状态的技术可能提升生成内容的准确性和一致性，这对推荐和搜索系统的质量至关重要。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 06:38:13
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11052v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11052v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    Autoregressive (AR) models remain the standard for natural language generation but still suffer from high latency due to strictly sequential decoding. Recent diffusion-inspired approaches, such as LlaDA and Dream, mitigate this by generating in parallel, yet they suffer from two core limitations: information loss, as predictive distributions for non-finalized tokens are discarded at each step, and premature commitment, where local decisions are made without sufficient global coordination. We introduce Latent Refinement Decoding (LRD), a two-stage framework with Latent Refinement and a Predictive Feedback Loop. The first stage maintains masked positions as distributional mixtures of predicted tokens and the mask embedding, allowing the model to establish more globally consistent beliefs. The second stage progressively finalizes confident tokens while retaining uncertain ones for iterative feedback. KL-divergence dynamics provide a principled and reliable criterion for convergence and early stopping. Experiments across coding (HumanEval +6.3, MBPP +2.6) and reasoning (GSM8K +2.9, MATH500 +3.8) show that LRD improves accuracy while delivering speedups of up to 10.6x, making it a strong and versatile alternative for parallel sequence generation.
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            <a href="https://www.alphaxiv.org/abs/2510.10977v1" target="_blank" rel="noopener noreferrer">
                重新审视模型插值以实现高效推理
            </a>
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        <div class="mb-2 text-base text-gray-700">
            Revisiting Model Interpolation for Efficient Reasoning
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Taiqiang Wu, Runming Yang, Tao Liu, Jiahao Wang, Ngai Wong
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">模型插值技术属于Transformer架构效率优化的范畴，通过组合不同模型实现更高效的推理。在推荐系统和搜索场景中，这种方法可以用于构建更高效的排序模型，在保持性能的同时降低推理延迟和计算成本，直接服务于大规模在线服务需求。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 03:30:01
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.10977v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.10977v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span></div>
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                    Model merging, typically on Instruct and Thinking models, has shown remarkable performance for efficient reasoning. In this paper, we systematically revisit the simplest merging method that interpolates two weights directly. Particularly, we observe that model interpolation follows a three-stage evolutionary paradigm with distinct behaviors on the reasoning trajectory. These dynamics provide a principled guide for navigating the performance-cost trade-off. Empirical results demonstrate that a strategically interpolated model surprisingly surpasses sophisticated model merging baselines on both efficiency and effectiveness. We further validate our findings with extensive ablation studies on model layers, modules, and decoding strategies. Ultimately, this work demystifies model interpolation and offers a practical framework for crafting models with precisely targeted reasoning capabilities. Code is available at \href{https://github.com/wutaiqiang/MI}{Github}.
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            <a href="https://www.alphaxiv.org/abs/2510.11115v1" target="_blank" rel="noopener noreferrer">
                连接巨人：大型多模态模型的协同知识迁移用于少样本学习
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            <i class="fa fa-star mr-1"></i>7/10
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            Connecting Giants: Synergistic Knowledge Transfer of Large Multimodal Models for Few-Shot Learning
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Hao Tang, Shengfeng He, Jing Qin
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文涉及大型多模态模型的知识迁移和少样本学习，这属于'赋能LLM技术'范畴。在推荐系统、搜索和广告中，这种技术可以用于快速适应新用户、新物品或新场景，通过少量样本实现个性化模型调整，解决冷启动和长尾问题。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 08:06:23
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11115v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11115v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.MM</span></div>
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                    Few-shot learning (FSL) addresses the challenge of classifying novel classes with limited training samples. While some methods leverage semantic knowledge from smaller-scale models to mitigate data scarcity, these approaches often introduce noise and bias due to the data's inherent simplicity. In this paper, we propose a novel framework, Synergistic Knowledge Transfer (SynTrans), which effectively transfers diverse and complementary knowledge from large multimodal models to empower the off-the-shelf few-shot learner. Specifically, SynTrans employs CLIP as a robust teacher and uses a few-shot vision encoder as a weak student, distilling semantic-aligned visual knowledge via an unsupervised proxy task. Subsequently, a training-free synergistic knowledge mining module facilitates collaboration among large multimodal models to extract high-quality semantic knowledge. Building upon this, a visual-semantic bridging module enables bi-directional knowledge transfer between visual and semantic spaces, transforming explicit visual and implicit semantic knowledge into category-specific classifier weights. Finally, SynTrans introduces a visual weight generator and a semantic weight reconstructor to adaptively construct optimal multimodal FSL classifiers. Experimental results on four FSL datasets demonstrate that SynTrans, even when paired with a simple few-shot vision encoder, significantly outperforms current state-of-the-art methods.
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            <a href="https://www.alphaxiv.org/abs/2510.10986v1" target="_blank" rel="noopener noreferrer">
                Mixup技术有助于更好地理解多模态视频
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            Mixup Helps Understanding Multimodal Video Better
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xiaoyu Ma, Ding Ding, Hao Chen
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文涉及多模态理解技术，与VLM类比处理异构数据的焦点相关。Mixup作为一种数据增强技术，在多模态视频场景中的应用潜力可迁移到推荐系统中处理用户行为序列、上下文特征等多模态数据，提升模型对复杂用户意图的理解能力。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 03:53:25
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.10986v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.10986v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Multimodal video understanding plays a crucial role in tasks such as action recognition and emotion classification by combining information from different modalities. However, multimodal models are prone to overfitting strong modalities, which can dominate learning and suppress the contributions of weaker ones. To address this challenge, we first propose Multimodal Mixup (MM), which applies the Mixup strategy at the aggregated multimodal feature level to mitigate overfitting by generating virtual feature-label pairs. While MM effectively improves generalization, it treats all modalities uniformly and does not account for modality imbalance during training. Building on MM, we further introduce Balanced Multimodal Mixup (B-MM), which dynamically adjusts the mixing ratios for each modality based on their relative contributions to the learning objective. Extensive experiments on several datasets demonstrate the effectiveness of our methods in improving generalization and multimodal robustness.
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            <a href="https://www.alphaxiv.org/abs/2510.10978v1" target="_blank" rel="noopener noreferrer">
                LLM是否关注正确的词语？诊断基于LLM的推荐系统中的语言偏见
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            <i class="fa fa-star mr-1"></i>6/10
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            Does LLM Focus on the Right Words? Diagnosing Language Bias in LLM-based Recommenders
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Bohao Wang, Jiawei Chen, Feng Liu, Changwang Zhang, Jun Wang, Canghong Jin, Chun...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文直接涉及LLM在推荐系统中的应用，属于'Direct LLM Applications'范畴。然而，它主要关注语言偏见诊断，这偏向于公平性和评估方面，与排除的'公平性、伦理'等非技术主题有一定重叠，因此相关性受到限制。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 03:35:26
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.10978v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.10978v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.IR</span></div>
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                    Large language models (LLMs), owing to their extensive open-domain knowledge and semantic reasoning capabilities, have been increasingly integrated into recommender systems (RS). However, a substantial gap remains between the pre-training objectives of LLMs and the specific requirements of recommendation tasks. To address this gap, supervised fine-tuning (SFT) is commonly performed on specially curated recommendation datasets to further enhance their predictive ability. Despite its success, SFT exhibits a critical limitation: it induces Language Bias, whereby the model over-relies on auxiliary tokens-such as task descriptions and prefix-generated tokens-while underutilizing core user interaction tokens that encode user-specific preferences. This bias not only undermines recommendation accuracy but also raises unfairness concerns. To address this issue, we propose Group Distributionally Robust Optimization-based Tuning (GDRT), a novel fine-tuning paradigm that enforces consistent model performance across token groups with varying degrees of relevance to auxiliary tokens. By adaptively upweighting underperforming groups, typically those weakly correlated with auxiliary tokens, GDRT shifts the model's attention from superficial auxiliary cues to informative user interaction tokens, thereby mitigating language bias. Extensive experiments conducted on three public datasets demonstrate that GDRT effectively mitigates language bias, yielding substantial improvements in recommendation accuracy (with an average NDCG@10 gain of 24.29%) and significantly enhancing recommendation fairness.
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            <a href="https://www.alphaxiv.org/abs/2510.10991v1" target="_blank" rel="noopener noreferrer">
                智能体多模态大语言模型综述
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            A Survey on Agentic Multimodal Large Language Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Huanjin Yao, Ruifei Zhang, Jiaxing Huang, Jingyi Zhang, Yibo Wang, Bo Fang, Ruol...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文探讨多模态LLM与智能体系统的结合，属于'赋能LLM技术'范畴。多模态智能体可应用于搜索和推荐系统，通过整合视觉、文本等多源信息来增强用户理解和交互能力，例如在电商推荐中处理商品图像与用户文本查询的复杂交互。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 04:07:01
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.10991v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.10991v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span></div>
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                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    With the recent emergence of revolutionary autonomous agentic systems, research community is witnessing a significant shift from traditional static, passive, and domain-specific AI agents toward more dynamic, proactive, and generalizable agentic AI. Motivated by the growing interest in agentic AI and its potential trajectory toward AGI, we present a comprehensive survey on Agentic Multimodal Large Language Models (Agentic MLLMs). In this survey, we explore the emerging paradigm of agentic MLLMs, delineating their conceptual foundations and distinguishing characteristics from conventional MLLM-based agents. We establish a conceptual framework that organizes agentic MLLMs along three fundamental dimensions: (i) Agentic internal intelligence functions as the system's commander, enabling accurate long-horizon planning through reasoning, reflection, and memory; (ii) Agentic external tool invocation, whereby models proactively use various external tools to extend their problem-solving capabilities beyond their intrinsic knowledge; and (iii) Agentic environment interaction further situates models within virtual or physical environments, allowing them to take actions, adapt strategies, and sustain goal-directed behavior in dynamic real-world scenarios. To further accelerate research in this area for the community, we compile open-source training frameworks, training and evaluation datasets for developing agentic MLLMs. Finally, we review the downstream applications of agentic MLLMs and outline future research directions for this rapidly evolving field. To continuously track developments in this rapidly evolving field, we will also actively update a public repository at https://github.com/HJYao00/Awesome-Agentic-MLLMs.
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            <a href="https://www.alphaxiv.org/abs/2510.11496v1" target="_blank" rel="noopener noreferrer">
                AndesVL技术报告：一种高效的移动端多模态大语言模型
            </a>
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        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>6/10
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        <div class="mb-2 text-base text-gray-700">
            AndesVL Technical Report: An Efficient Mobile-side Multimodal Large Language Model
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zhiwei Jin, Xiaohui Song, Nan Wang, Yafei Liu, Chao Li, Xin Li, Ruichen Wang, Zh...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于移动端高效多模态大语言模型，属于'Enabling LLM Tech'范畴，通过模型效率优化支持移动设备部署。在推荐系统、搜索和广告领域，这种高效多模态模型可应用于移动端的个性化推荐、多模态搜索和广告内容理解，提升用户体验和系统性能。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 15:04:38
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11496v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11496v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    In recent years, while cloud-based MLLMs such as QwenVL, InternVL, GPT-4o, Gemini, and Claude Sonnet have demonstrated outstanding performance with enormous model sizes reaching hundreds of billions of parameters, they significantly surpass the limitations in memory, power consumption, and computing capacity of edge devices such as mobile phones. This paper introduces AndesVL, a suite of mobile-side MLLMs with 0.6B to 4B parameters based on Qwen3's LLM and various visual encoders. We comprehensively outline the model architectures, training pipeline, and training data of AndesVL, which achieves first-tier performance across a wide range of open-source benchmarks, including fields such as text-rich image understanding, reasoning and math, multi-image comprehension, general VQA, hallucination mitigation, multilingual understanding, and GUI-related tasks when compared with state-of-the-art models of a similar scale. Furthermore, we introduce a 1+N LoR
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            <a href="https://www.alphaxiv.org/abs/2510.11599v1" target="_blank" rel="noopener noreferrer">
                SemCSE-Multi：用于特定方面和可解释科学领域映射的多方面可解码嵌入
            </a>
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            <i class="fa fa-star mr-1"></i>3/10
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        <div class="mb-2 text-base text-gray-700">
            SemCSE-Multi: Multifaceted and Decodable Embeddings for Aspect-Specific and Interpretable Scientific Domain Mapping
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Marc Brinner, Sina Zarrieß
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注科学领域映射和多方面嵌入，这与推荐系统中的多兴趣建模有一定相似性。然而，论文明确针对科学领域应用，缺乏明确的推荐、搜索或广告应用场景。虽然可解码嵌入技术可能对解释性推荐系统有潜在价值，但论文标题未表明与目标领域的直接关联。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 16:38:20
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11599v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11599v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.IR</span><span class="category-tag">cs.LG</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    We propose SemCSE-Multi, a novel unsupervised framework for generating multifaceted embeddings of scientific abstracts, evaluated in the domains of invasion biology and medicine. These embeddings capture distinct, individually specifiable aspects in isolation, thus enabling fine-grained and controllable similarity assessments as well as adaptive, user-driven visualizations of scientific domains. Our approach relies on an unsupervised procedure that produces aspect-specific summarizing sentences and trains embedding models to map semantically related summaries to nearby positions in the embedding space. We then distill these aspect-specific embedding capabilities into a unified embedding model that directly predicts multiple aspect embeddings from a scientific abstract in a single, efficient forward pass. In addition, we introduce an embedding decoding pipeline that decodes embeddings back into natural language descriptions of their associated aspects. Notably, we show that this decoding remains effective even for unoccupied regions in low-dimensional visualizations, thus offering vastly improved interpretability in user-centric settings.
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            <a href="https://www.alphaxiv.org/abs/2510.11394v1" target="_blank" rel="noopener noreferrer">
                VeriCite：通过严格验证在检索增强生成中实现可靠引用
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            <i class="fa fa-star mr-1"></i>3/10
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            VeriCite: Towards Reliable Citations in Retrieval-Augmented Generation via Rigorous Verification
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Haosheng Qian, Yixing Fan, Jiafeng Guo, Ruqing Zhang, Qi Chen, Dawei Yin, Xueqi ...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注检索增强生成(RAG)中的引用可靠性验证，这属于LLM评估和可信度范畴。虽然RAG技术在搜索系统中有关联，但论文聚焦于引用验证而非核心搜索/推荐算法改进，且验证方法对RecSys/Ads的直接应用潜力有限。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 13:38:54
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11394v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11394v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.IR</span></div>
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                    Retrieval-Augmented Generation (RAG) has emerged as a crucial approach for enhancing the responses of large language models (LLMs) with external knowledge sources. Despite the impressive performance in complex question-answering tasks, RAG still struggles with hallucinations. Attributing RAG-generated content through in-line citations has demonstrated potential in reducing hallucinations and facilitating human verification. Existing citation generation methods primarily rely on either fine-tuning the generator or employing post-processing approaches for citation matching. However, the former approach demands substantial annotated data and computational resources, while the latter often encounters difficulties in managing multiple citations and frequently produces suboptimal results. In this paper, we introduce a novel framework, called VeriCite, designed to rigorously validate supporting evidence and enhance answer attribution. Specifically, VeriCite breaks down into a three-stage generation: 1) The initial answer generation first generates a response based on all available contexts and has its claims verified through the NLI model; 2) the supporting evidence selection assesses the utility of each document and extracts useful supporting evidences; 3) the final answer refinement integrates the initial response and collected evidences to produce the final, refined answer.We conduct experiments across five open-source LLMs and four datasets, demonstrating that VeriCite can significantly improve citation quality while maintaining the correctness of the answers.
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            <a href="https://www.alphaxiv.org/abs/2510.11323v1" target="_blank" rel="noopener noreferrer">
                基于动态网络的两阶段时间序列预测在联盟营销中的应用
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            Dynamic Network-Based Two-Stage Time Series Forecasting for Affiliate Marketing
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zhe Wang, Yaming Yang, Ziyu Guan, Bin Tong, Rui Wang, Wei Zhao, Hongbo Deng
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文涉及时间序列预测和营销应用，但主要聚焦于联盟营销这一特定领域，而非核心的推荐系统、搜索或广告排名技术。虽然时间序列预测在推荐/广告中有应用潜力，但论文标题未表明涉及LLM、Transformer架构或异构数据统一建模等当前关注的核心技术方向。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 12:21:29
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11323v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11323v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.IR</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    In recent years, affiliate marketing has emerged as a revenue-sharing strategy where merchants collaborate with promoters to promote their products. It not only increases product exposure but also allows promoters to earn a commission. This paper addresses the pivotal yet under-explored challenge in affiliate marketing: accurately assessing and predicting the contributions of promoters in product promotion. We design a novel metric for evaluating the indirect contributions of the promoter, called propagation scale. Unfortunately, existing time series forecasting techniques fail to deliver accurate predictions due to the propagation scale being influenced by multiple factors and the inherent complexities arising from dynamic scenarios. To address this issue, we decouple the network structure from the node signals and propose a two-stage solution: initially, the basic self-sales and network structure prediction are conducted separately, followed by the synthesis of the propagation scale. Specifically, we design a graph convolution encoding scheme based on descendant neighbors and incorporate hypergraph convolution to efficiently capture complex promotional dynamics. Additionally, three auxiliary tasks are employed: self-sales prediction for base estimations, descendant prediction to synthesize propagation scale, and promoter activation prediction to mitigate high volatility issues. Extensive offline experiments on large-scale industrial datasets validate the superiority of our method. We further deploy our model on Alimama platform with over $100,000$ promoters, achieving a $9.29\%$ improvement in GMV and a $5.89\%$ increase in sales volume.
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            <a href="https://www.alphaxiv.org/abs/2510.11713v1" target="_blank" rel="noopener noreferrer">
                大型推理模型是否可中断？
            </a>
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            <i class="fa fa-star mr-1"></i>3/10
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        <div class="mb-2 text-base text-gray-700">
            Are Large Reasoning Models Interruptible?
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Tsung-Han Wu, Mihran Miroyan, David M. Chan, Trevor Darrell, Narges Norouzi, Jos...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文探讨推理模型的可中断性，这属于LLM推理能力的基础研究。虽然推理能力对搜索和推荐系统中的复杂查询处理有潜在应用，但论文焦点更偏向通用推理能力而非具体的RecSys/Search/Ads应用，且未明确涉及Transformer架构改进或多模态建模等核心关注点。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 17:59:35
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11713v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11713v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.LG</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Large Reasoning Models (LRMs) excel at complex reasoning but are traditionally evaluated in static, "frozen world" settings: model responses are assumed to be instantaneous, and the context of a request is presumed to be immutable over the duration of the response. While generally true for short-term tasks, the "frozen world" assumption breaks down in modern reasoning tasks such as assistive programming, where models may take hours to think through problems and code may change dramatically from the time the model starts thinking to the model's final output. In this work, we challenge the frozen world assumption and evaluate LRM robustness under two realistic dynamic scenarios: interruptions, which test the quality of the model's partial outputs on a limited budget, and dynamic context, which tests model adaptation to in-flight changes. Across mathematics and programming benchmarks that require long-form reasoning, static evaluations consistently overestimate robustness: even state-of-the-art LRMs, which achieve high accuracy in static settings, can fail unpredictably when interrupted or exposed to changing context, with performance dropping by up to 60% when updates are introduced late in the reasoning process. Our analysis further reveals several novel failure modes, including reasoning leakage, where models fold the reasoning into their final answer when interrupted; panic, where under time pressure models abandon reasoning entirely and return incorrect answers; and self-doubt, where performance degrades while incorporating updated information.
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            <a href="https://www.alphaxiv.org/abs/2510.11482v1" target="_blank" rel="noopener noreferrer">
                研究大型语言模型在文本预处理中的语言能力
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            <i class="fa fa-star mr-1"></i>3/10
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            Investigating Large Language Models' Linguistic Abilities for Text Preprocessing
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Marco Braga, Gian Carlo Milanese, Gabriella Pasi
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文聚焦于LLM在文本预处理中的语言能力研究，属于LLM技术基础进展。虽然文本预处理是搜索和推荐系统中的重要环节，但论文标题未明确指向搜索/推荐/广告领域的特定应用，且可能更偏向NLP技术本身而非领域应用。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 14:53:44
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11482v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11482v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Text preprocessing is a fundamental component of Natural Language Processing, involving techniques such as stopword removal, stemming, and lemmatization to prepare text as input for further processing and analysis. Despite the context-dependent nature of the above techniques, traditional methods usually ignore contextual information. In this paper, we investigate the idea of using Large Language Models (LLMs) to perform various preprocessing tasks, due to their ability to take context into account without requiring extensive language-specific annotated resources. Through a comprehensive evaluation on web-sourced data, we compare LLM-based preprocessing (specifically stopword removal, lemmatization and stemming) to traditional algorithms across multiple text classification tasks in six European languages. Our analysis indicates that LLMs are capable of replicating traditional stopword removal, lemmatization, and stemming methods with accuracies reaching 97%, 82%, and 74%, respectively. Additionally, we show that ML algorithms trained on texts preprocessed by LLMs achieve an improvement of up to 6% with respect to the $F_1$ measure compared to traditional techniques. Our code, prompts, and results are publicly available at https://github.com/GianCarloMilanese/llm_pipeline_wi-iat.
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            <a href="https://www.alphaxiv.org/abs/2510.11372v1" target="_blank" rel="noopener noreferrer">
                领域适应和指令微调中记忆化的早期检测与减少
            </a>
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        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>3/10
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        <div class="mb-2 text-base text-gray-700">
            Early Detection and Reduction of Memorisation for Domain Adaptation and Instruction Tuning
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Dean L. Slack, Noura Al Moubayed
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注LLM训练中的记忆化问题，这属于LLM安全/隐私范畴，而非核心推荐/搜索/广告技术。虽然指令微调是LLM应用的重要环节，但论文焦点在记忆化检测与减少这一非技术性问题上，与您关注的领域核心进展、架构创新或直接应用相关性较弱。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 13:12:46
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11372v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11372v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                    Although large language models excel across many tasks, they can memorise training data and thereby expose private or copyrighted text. Most defences target the pre-training stage, leaving memorisation during fine-tuning, especially for domain adaptation and instruction tuning, poorly understood. We fine-tune Pythia, Llama3, and Mistral models spanning 1.4B-70B parameters on common evaluation datasets and track verbatim memorisation throughout training. We find that memorisation increases dramatically in the first few epochs, often significantly before either validation perplexity or evaluation performance is optimised. We use a simple but effective n-gram memorisation score which reliably precedes verbatim memorisation; using it as an early-stopping criterion mitigates memorisation with minimal performance loss. Further, we introduce an n-gram-aware loss regulariser and show that it reduces memorisation across all model families tested by up to 40% while minimising evaluation performance trade-offs when compared to an existing memorisation mitigation strategy. These results yield practical, scalable insights into memorisation dynamics during language model fine-tuning.
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            <a href="https://www.alphaxiv.org/abs/2510.11328v1" target="_blank" rel="noopener noreferrer">
                大型语言模型是否“感受”情感？情感回路发现与控制
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            <i class="fa fa-star mr-1"></i>3/10
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        <div class="mb-2 text-base text-gray-700">
            Do LLMs "Feel"? Emotion Circuits Discovery and Control
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Chenxi Wang, Yixuan Zhang, Ruiji Yu, Yufei Zheng, Lang Gao, Zirui Song, Zixiang ...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要研究LLMs中的情感回路发现与控制，属于LLM内部机制探索，与推荐系统、搜索或广告的直接应用关联较弱。虽然情感理解在个性化推荐中有潜在价值，但论文更侧重于基础情感机制而非具体应用场景，因此相关性有限。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 12:24:24
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11328v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11328v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    As the demand for emotional intelligence in large language models (LLMs) grows, a key challenge lies in understanding the internal mechanisms that give rise to emotional expression and in controlling emotions in generated text. This study addresses three core questions: (1) Do LLMs contain context-agnostic mechanisms shaping emotional expression? (2) What form do these mechanisms take? (3) Can they be harnessed for universal emotion control? We first construct a controlled dataset, SEV (Scenario-Event with Valence), to elicit comparable internal states across emotions. Subsequently, we extract context-agnostic emotion directions that reveal consistent, cross-context encoding of emotion (Q1). We identify neurons and attention heads that locally implement emotional computation through analytical decomposition and causal analysis, and validate their causal roles via ablation and enhancement interventions. Next, we quantify each sublayer's causal influence on the model's final emotion representation and integrate the identified local components into coherent global emotion circuits that drive emotional expression (Q2). Directly modulating these circuits achieves 99.65% emotion-expression accuracy on the test set, surpassing prompting- and steering-based methods (Q3). To our knowledge, this is the first systematic study to uncover and validate emotion circuits in LLMs, offering new insights into interpretability and controllable emotional intelligence.
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            <a href="https://www.alphaxiv.org/abs/2510.11221v1" target="_blank" rel="noopener noreferrer">
                WebRouter：基于变分信息瓶颈的查询特定路由器，用于成本敏感的Web代理
            </a>
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            <i class="fa fa-star mr-1"></i>3/10
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            WebRouter: Query-specific Router via Variational Information Bottleneck for Cost-sensitive Web Agent
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Tao Li, Jinlong Hu, Yang Wang, Junfeng Liu, Xuejun Liu
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文涉及查询路由和成本敏感决策，与搜索系统中的查询路由和资源分配有一定相关性。然而，它主要关注Web代理场景，与推荐系统、搜索排名或广告的核心技术关联较弱，且未明确涉及LLM或Transformer架构的进展。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 10:05:43
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11221v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11221v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    LLM-brained web agents offer powerful capabilities for web automation but face a critical cost-performance trade-off. The challenge is amplified by web agents' inherently complex prompts that include goals, action histories, and environmental states, leading to degraded LLM ensemble performance. To address this, we introduce WebRouter, a novel query-specific router trained from an information-theoretic perspective. Our core contribution is a cost-aware Variational Information Bottleneck (ca-VIB) objective, which learns a compressed representation of the input prompt while explicitly penalizing the expected operational cost. Experiments on five real-world websites from the WebVoyager benchmark show that WebRouter reduces operational costs by a striking 87.8\% compared to a GPT-4o baseline, while incurring only a 3.8\% accuracy drop.
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            <a href="https://www.alphaxiv.org/abs/2510.11160v1" target="_blank" rel="noopener noreferrer">
                一刀切不可行：探索基于距离的多标签文本分类中的可变阈值
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            <i class="fa fa-star mr-1"></i>3/10
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            One Size Does Not Fit All: Exploring Variable Thresholds for Distance-Based Multi-Label Text Classification
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jens Van Nooten, Andriy Kosar, Guy De Pauw, Walter Daelemans
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要研究多标签文本分类中的阈值优化问题，属于传统NLP技术范畴。虽然文本分类在搜索系统中可能有应用（如文档分类），但论文没有明确涉及推荐系统、广告或Transformer架构的核心进展，与当前关注的LLM技术、推荐系统核心算法或异构数据统一建模等焦点领域关联度较低。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 08:52:14
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11160v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11160v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Distance-based unsupervised text classification is a method within text classification that leverages the semantic similarity between a label and a text to determine label relevance. This method provides numerous benefits, including fast inference and adaptability to expanding label sets, as opposed to zero-shot, few-shot, and fine-tuned neural networks that require re-training in such cases. In multi-label distance-based classification and information retrieval algorithms, thresholds are required to determine whether a text instance is "similar" to a label or query. Similarity between a text and label is determined in a dense embedding space, usually generated by state-of-the-art sentence encoders. Multi-label classification complicates matters, as a text instance can have multiple true labels, unlike in multi-class or binary classification, where each instance is assigned only one label. We expand upon previous literature on this underexplored topic by thoroughly examining and evaluating the ability of sentence encoders to perform distance-based classification. First, we perform an exploratory study to verify whether the semantic relationships between texts and labels vary across models, datasets, and label sets by conducting experiments on a diverse collection of realistic multi-label text classification (MLTC) datasets. We find that similarity distributions show statistically significant differences across models, datasets and even label sets. We propose a novel method for optimizing label-specific thresholds using a validation set. Our label-specific thresholding method achieves an average improvement of 46% over normalized 0.5 thresholding and outperforms uniform thresholding approaches from previous work by an average of 14%. Additionally, the method demonstrates strong performance even with limited labeled examples.
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            <a href="https://www.alphaxiv.org/abs/2510.11031v1" target="_blank" rel="noopener noreferrer">
                LogiNumSynth：为语言模型合成联合逻辑-数值推理问题
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            LogiNumSynth: Synthesizing Joint Logical-Numerical Reasoning Problems for Language Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yiwei Liu, Yucheng Li, Xiao Li, Gong Cheng
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于逻辑-数值推理问题的合成，属于LLM推理能力的基础研究。虽然逻辑推理能力对于搜索和推荐系统中的复杂查询理解有一定潜在价值，但论文本身没有明确展示在RecSys/Search/Ads领域的直接应用场景，相关性较为间接。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 06:01:02
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11031v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11031v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Joint logical-numerical reasoning remains a major challenge for language models, yet existing datasets rely on fixed rule sets and offer limited control over task complexity, constraining their generalizability for evaluation and training. We present LogiNumSynth, a flexible natural language problem synthesizer that synthesizes tasks requiring proficiency in joint logical reasoning (e.g., rule-based reasoning) and numerical reasoning (e.g., arithmetic computation). LogiNumSynth supports fine-grained control over reasoning world richness, logical reasoning depth, and the complexity of numerical computations, enabling flexible data synthesis across difficulty levels. We demonstrate three key contributions: (1) Synthesizer -- synthesizing fully controllable joint reasoning tasks over natural language; (2) Evaluation & Process Analysis -- evaluating both process accuracy and answer accuracy; (3) Targeted Training -- using synthesized data to enhance LLMs' reasoning performance. Experiments with multiple LLMs highlight persistent weaknesses in logical-numerical reasoning, showing that LogiNumSynth can serve as both a diagnostic tool and a source of targeted supervision for advancing integrated reasoning skills.
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            <a href="https://www.alphaxiv.org/abs/2510.10913v1" target="_blank" rel="noopener noreferrer">
                ADVICE：答案依赖的言语化置信度估计
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            <i class="fa fa-star mr-1"></i>3/10
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        <div class="mb-2 text-base text-gray-700">
            ADVICE: Answer-Dependent Verbalized Confidence Estimation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Ki Jung Seo, Sehun Lim, Taeuk Kim
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文关注LLM置信度估计，属于Enabling LLM Tech范畴。在搜索和推荐系统中，准确的置信度估计可用于结果排序、不确定性感知的推荐以及查询理解优化。然而，该方法更偏向通用NLP技术，与推荐/搜索/广告的核心排序问题关联度有限。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 02:18:33
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.10913v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.10913v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    Recent progress in large language models (LLMs) has enabled them to express their confidence in natural language, enhancing transparency and reliability. However, their confidence often exhibits overconfidence, the cause of which remains poorly understood. In this work, we conduct a detailed analysis of the dynamics underlying verbalized confidence and identify answer-independence as a key factor, defined as the model's failure to condition confidence on its own answer. To address this, we propose ADVICE (Answer-Dependent Verbalized Confidence Estimation), a fine-tuning framework that facilitates answer-grounded confidence estimation. Extensive experiments show that ADVICE substantially improves confidence calibration while preserving task performance. Further analyses confirm that ADVICE strengthens answer-groundedness, leading to more balanced and well-calibrated confidence distributions. Our findings shed light on the origin of overconfidence and establish a framework for more trustworthy confidence verbalization.
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            <a href="https://www.alphaxiv.org/abs/2510.11690v1" target="_blank" rel="noopener noreferrer">
                基于表示自编码器的扩散变换器
            </a>
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            <i class="fa fa-star mr-1"></i>3/10
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        <div class="mb-2 text-base text-gray-700">
            Diffusion Transformers with Representation Autoencoders
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Boyang Zheng, Nanye Ma, Shengbang Tong, Saining Xie
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出了扩散变换器与表示自编码器的结合，属于Transformer架构效率方面的进展，可能应用于推荐系统中的序列建模或特征表示学习。然而，扩散模型主要关注生成建模，与排名、检索等核心任务的相关性较弱，应用潜力有限。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 17:51:39
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11690v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11690v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.LG</span></div>
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                    Latent generative modeling, where a pretrained autoencoder maps pixels into a latent space for the diffusion process, has become the standard strategy for Diffusion Transformers (DiT); however, the autoencoder component has barely evolved. Most DiTs continue to rely on the original VAE encoder, which introduces several limitations: outdated backbones that compromise architectural simplicity, low-dimensional latent spaces that restrict information capacity, and weak representations that result from purely reconstruction-based training and ultimately limit generative quality. In this work, we explore replacing the VAE with pretrained representation encoders (e.g., DINO, SigLIP, MAE) paired with trained decoders, forming what we term Representation Autoencoders (RAEs). These models provide both high-quality reconstructions and semantically rich latent spaces, while allowing for a scalable transformer-based architecture. Since these latent spaces are typically high-dimensional, a key challenge is enabling diffusion transformers to operate effectively within them. We analyze the sources of this difficulty, propose theoretically motivated solutions, and validate them empirically. Our approach achieves faster convergence without auxiliary representation alignment losses. Using a DiT variant equipped with a lightweight, wide DDT head, we achieve strong image generation results on ImageNet: 1.51 FID at 256x256 (no guidance) and 1.13 at both 256x256 and 512x512 (with guidance). RAE offers clear advantages and should be the new default for diffusion transformer training.
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            <a href="https://www.alphaxiv.org/abs/2510.11675v1" target="_blank" rel="noopener noreferrer">
                FACE：忠实自动概念提取
            </a>
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        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>3/10
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            FACE: Faithful Automatic Concept Extraction
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Dipkamal Bhusal, Michael Clifford, Sara Rampazzi, Nidhi Rastogi
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文涉及概念提取技术，这在推荐系统或搜索中可能用于理解用户意图或项目特征。然而，标题没有明确说明与LLM、Transformer架构或异构数据建模的直接联系，且概念提取本身是一个相对通用的技术，缺乏明确的RecSys/Search/Ads应用背景。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 17:44:45
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11675v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11675v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    Interpreting deep neural networks through concept-based explanations offers a bridge between low-level features and high-level human-understandable semantics. However, existing automatic concept discovery methods often fail to align these extracted concepts with the model's true decision-making process, thereby compromising explanation faithfulness. In this work, we propose FACE (Faithful Automatic Concept Extraction), a novel framework that augments Non-negative Matrix Factorization (NMF) with a Kullback-Leibler (KL) divergence regularization term to ensure alignment between the model's original and concept-based predictions. Unlike prior methods that operate solely on encoder activations, FACE incorporates classifier supervision during concept learning, enforcing predictive consistency and enabling faithful explanations. We provide theoretical guarantees showing that minimizing the KL divergence bounds the deviation in predictive distributions, thereby promoting faithful local linearity in the learned concept space. Systematic evaluations on ImageNet, COCO, and CelebA datasets demonstrate that FACE outperforms existing methods across faithfulness and sparsity metrics.
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            <a href="https://www.alphaxiv.org/abs/2510.11579v1" target="_blank" rel="noopener noreferrer">
                MS-Mix：揭示Mixup在多模态情感分析中的强大能力
            </a>
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            <i class="fa fa-star mr-1"></i>3/10
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            MS-Mix: Unveiling the Power of Mixup for Multimodal Sentiment Analysis
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Hongyu Zhu, Lin Chen, Mounim A. El-Yacoubi, Mingsheng Shang
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">虽然该论文涉及多模态学习和数据增强技术（Mixup），但其核心应用领域是多模态情感分析，这属于纯粹的NLP应用范畴。Mixup技术本身在理论上可能应用于推荐系统中的多模态特征融合，但论文标题明确限定于情感分析，与RecSys/Search/Ads的直接关联性较弱。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 16:23:32
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11579v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11579v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.LG</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Multimodal Sentiment Analysis (MSA) aims to identify and interpret human emotions by integrating information from heterogeneous data sources such as text, video, and audio. While deep learning models have advanced in network architecture design, they remain heavily limited by scarce multimodal annotated data. Although Mixup-based augmentation improves generalization in unimodal tasks, its direct application to MSA introduces critical challenges: random mixing often amplifies label ambiguity and semantic inconsistency due to the lack of emotion-aware mixing mechanisms. To overcome these issues, we propose MS-Mix, an adaptive, emotion-sensitive augmentation framework that automatically optimizes sample mixing in multimodal settings. The key components of MS-Mix include: (1) a Sentiment-Aware Sample Selection (SASS) strategy that effectively prevents semantic confusion caused by mixing samples with contradictory emotions. (2) a Sentiment Intensity Guided (SIG) module using multi-head self-attention to compute modality-specific mixing ratios dynamically based on their respective emotional intensities. (3) a Sentiment Alignment Loss (SAL) that aligns the prediction distributions across modalities, and incorporates the Kullback-Leibler-based loss as an additional regularization term to train the emotion intensity predictor and the backbone network jointly. Extensive experiments on three benchmark datasets with six state-of-the-art backbones confirm that MS-Mix consistently outperforms existing methods, establishing a new standard for robust multimodal sentiment augmentation. The source code is available at: https://github.com/HongyuZhu-s/MS-Mix.
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            <a href="https://www.alphaxiv.org/abs/2510.11296v1" target="_blank" rel="noopener noreferrer">
                ΔEnergy：在视觉-语言对齐过程中优化能量变化可同时改善分布外检测与分布外泛化
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            $Δ\mathrm{Energy}$: Optimizing Energy Change During Vision-Language Alignment Improves both OOD Detection and OOD Generalization
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Lin Zhu, Yifeng Yang, Xinbing Wang, Qinying Gu, Nanyang Ye
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1"></p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 11:36:58
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11296v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11296v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.LG</span></div>
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                    Recent approaches for vision-language models (VLMs) have shown remarkable success in achieving fast downstream adaptation. When applied to real-world downstream tasks, VLMs inevitably encounter both the in-distribution (ID) data and out-of-distribution (OOD) data. The OOD datasets often include both covariate shifts (e.g., known classes with changes in image styles) and semantic shifts (e.g., test-time unseen classes). This highlights the importance of improving VLMs' generalization ability to covariate-shifted OOD data, while effectively detecting open-set semantic-shifted OOD classes. In this paper, inspired by the substantial energy change observed in closed-set data when re-aligning vision-language modalities (specifically by directly reducing the maximum cosine similarity to a low value), we introduce a novel OOD score, named {\Delta}Energy. {\Delta}Energy significantly outperforms the vanilla energy-based OOD score and provides a more reliable approach for OOD detection. Furthermore, {\Delta}Energy can simultaneously improve OOD generalization under covariate shifts, which is achieved by lower-bound maximization for {\Delta}Energy (termed EBM). EBM is theoretically proven to not only enhance OOD detection but also yields a domain-consistent Hessian, which serves as a strong indicator for OOD generalization. Based on this finding, we developed a unified fine-tuning framework that allows for improving VLMs' robustness in both OOD generalization and OOD detection. Extensive experiments on challenging OOD detection and generalization benchmarks demonstrate the superiority of our method, outperforming recent approaches by 10% to 25% in AUROC.
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            <a href="https://www.alphaxiv.org/abs/2510.11106v1" target="_blank" rel="noopener noreferrer">
                组合式零样本学习：综述
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        <div class="mb-2 text-base text-gray-700">
            Compositional Zero-Shot Learning: A Survey
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Ans Munir, Faisal Z. Qureshi, Mohsen Ali, Muhammad Haris Khan
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文是关于组合式零样本学习的综述，主要涉及视觉和语言领域的组合泛化能力。虽然组合学习概念在推荐系统中可能有潜在应用（如处理新用户-物品组合），但该论文更偏向计算机视觉和基础机器学习领域，与推荐系统、搜索或广告的直接关联较弱，且未明确涉及Transformer架构或LLM技术。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 07:54:47
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11106v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11106v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Compositional Zero-Shot Learning (CZSL) is a critical task in computer vision that enables models to recognize unseen combinations of known attributes and objects during inference, addressing the combinatorial challenge of requiring training data for every possible composition. This is particularly challenging because the visual appearance of primitives is highly contextual; for example, ``small'' cats appear visually distinct from ``older'' ones, and ``wet'' cars differ significantly from ``wet'' cats. Effectively modeling this contextuality and the inherent compositionality is crucial for robust compositional zero-shot recognition. This paper presents, to our knowledge, the first comprehensive survey specifically focused on Compositional Zero-Shot Learning. We systematically review the state-of-the-art CZSL methods, introducing a taxonomy grounded in disentanglement, with four families of approaches: no explicit disentanglement, textual disentanglement, visual disentanglement, and cross-modal disentanglement. We provide a detailed comparative analysis of these methods, highlighting their core advantages and limitations in different problem settings, such as closed-world and open-world CZSL. Finally, we identify the most significant open challenges and outline promising future research directions. This survey aims to serve as a foundational resource to guide and inspire further advancements in this fascinating and important field. Papers studied in this survey with their official code are available on our github: https://github.com/ans92/Compositional-Zero-Shot-Learning
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                基于检测变换器的无源目标检测
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            Source-Free Object Detection with Detection Transformer
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Huizai Yao, Sicheng Zhao, Shuo Lu, Hui Chen, Yangyang Li, Guoping Liu, Tengfei X...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注计算机视觉中的目标检测任务，虽然使用了Transformer架构，但其核心是源自由（source-free）设置下的视觉检测问题。在推荐系统、搜索或广告领域，这种纯粹的视觉检测技术缺乏直接的应用场景，无法为这些领域的核心问题（如排序、用户建模等）提供明确的解决方案。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 07:35:04
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11090v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11090v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Source-Free Object Detection (SFOD) enables knowledge transfer from a source domain to an unsupervised target domain for object detection without access to source data. Most existing SFOD approaches are either confined to conventional object detection (OD) models like Faster R-CNN or designed as general solutions without tailored adaptations for novel OD architectures, especially Detection Transformer (DETR). In this paper, we introduce Feature Reweighting ANd Contrastive Learning NetworK (FRANCK), a novel SFOD framework specifically designed to perform query-centric feature enhancement for DETRs. FRANCK comprises four key components: (1) an Objectness Score-based Sample Reweighting (OSSR) module that computes attention-based objectness scores on multi-scale encoder feature maps, reweighting the detection loss to emphasize less-recognized regions; (2) a Contrastive Learning with Matching-based Memory Bank (CMMB) module that integrates multi-level features into memory banks, enhancing class-wise contrastive learning; (3) an Uncertainty-weighted Query-fused Feature Distillation (UQFD) module that improves feature distillation through prediction quality reweighting and query feature fusion; and (4) an improved self-training pipeline with a Dynamic Teacher Updating Interval (DTUI) that optimizes pseudo-label quality. By leveraging these components, FRANCK effectively adapts a source-pre-trained DETR model to a target domain with enhanced robustness and generalization. Extensive experiments on several widely used benchmarks demonstrate that our method achieves state-of-the-art performance, highlighting its effectiveness and compatibility with DETR-based SFOD models.
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            <a href="https://www.alphaxiv.org/abs/2510.11028v1" target="_blank" rel="noopener noreferrer">
                增强零样本异常检测：CLIP-SAM协作与级联提示
            </a>
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        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>3/10
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        <div class="mb-2 text-base text-gray-700">
            Enhancing Zero-Shot Anomaly Detection: CLIP-SAM Collaboration with Cascaded Prompts
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yanning Hou, Ke Xu, Junfa Li, Yanran Ruan, Jianfeng Qiu
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注计算机视觉领域的异常检测，虽然使用了CLIP和SAM等基础模型，但其核心应用场景（视觉异常检测）与推荐系统、搜索或广告的直接关联性较弱。级联提示技术可能有潜力应用于多模态推荐中的特征交互，但这种联系较为间接且非论文主要贡献。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 05:53:49
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11028v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11028v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Recently, the powerful generalization ability exhibited by foundation models has brought forth new solutions for zero-shot anomaly segmentation tasks. However, guiding these foundation models correctly to address downstream tasks remains a challenge. This paper proposes a novel two-stage framework, for zero-shot anomaly segmentation tasks in industrial anomaly detection. This framework excellently leverages the powerful anomaly localization capability of CLIP and the boundary perception ability of SAM.(1) To mitigate SAM's inclination towards object segmentation, we propose the Co-Feature Point Prompt Generation (PPG) module. This module collaboratively utilizes CLIP and SAM to generate positive and negative point prompts, guiding SAM to focus on segmenting anomalous regions rather than the entire object. (2) To further optimize SAM's segmentation results and mitigate rough boundaries and isolated noise, we introduce the Cascaded Prompts for SAM (CPS) module. This module employs hybrid prompts cascaded with a lightweight decoder of SAM, achieving precise segmentation of anomalous regions. Across multiple datasets, consistent experimental validation demonstrates that our approach achieves state-of-the-art zero-shot anomaly segmentation results. Particularly noteworthy is our performance on the Visa dataset, where we outperform the state-of-the-art methods by 10.3\% and 7.7\% in terms of {$F_1$-max} and AP metrics, respectively.
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            <a href="https://www.alphaxiv.org/abs/2510.11654v1" target="_blank" rel="noopener noreferrer">
                FinVet：基于检索增强生成与外部事实核查代理协作框架的金融虚假信息检测
            </a>
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            <i class="fa fa-star mr-1"></i>2/10
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            FinVet: A Collaborative Framework of RAG and External Fact-Checking Agents for Financial Misinformation Detection
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Daniel Berhane Araya, Duoduo Liao
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注金融领域的虚假信息检测，属于特定领域应用，与推荐系统、搜索或广告的核心技术进展无关。虽然涉及RAG技术，但其应用场景局限于金融信息验证，缺乏在RecSys/Search/Ads领域的通用性或潜在应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 17:31:49
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11654v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11654v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.IR</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Financial markets face growing threats from misinformation that can trigger billions in losses in minutes. Most existing approaches lack transparency in their decision-making and provide limited attribution to credible sources. We introduce FinVet, a novel multi-agent framework that integrates two Retrieval-Augmented Generation (RAG) pipelines with external fact-checking through a confidence-weighted voting mechanism. FinVet employs adaptive three-tier processing that dynamically adjusts verification strategies based on retrieval confidence, from direct metadata extraction to hybrid reasoning to full model-based analysis. Unlike existing methods, FinVet provides evidence-backed verdicts, source attribution, confidence scores, and explicit uncertainty flags when evidence is insufficient. Experimental evaluation on the FinFact dataset shows that FinVet achieves an F1 score of 0.85, which is a 10.4% improvement over the best individual pipeline (fact-check pipeline) and 37% improvement over standalone RAG approaches.
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            <a href="https://www.alphaxiv.org/abs/2510.11701v1" target="_blank" rel="noopener noreferrer">
                揭秘智能体推理中的强化学习
            </a>
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Demystifying Reinforcement Learning in Agentic Reasoning
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zhaochen Yu, Ling Yang, Jiaru Zou, Shuicheng Yan, Mengdi Wang
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文聚焦于强化学习在智能体推理中的应用，属于纯粹的强化学习研究领域。虽然强化学习在推荐系统中偶有应用，但该标题没有明确指向推荐系统、搜索或广告的具体应用场景，也没有涉及LLM、Transformer架构或异构数据建模等核心技术方向。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 17:57:15
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11701v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11701v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Recently, the emergence of agentic RL has showcased that RL could also effectively improve the agentic reasoning ability of LLMs, yet the key design principles and optimal practices remain unclear. In this work, we conduct a comprehensive and systematic investigation to demystify reinforcement learning in agentic reasoning from three key perspectives: data, algorithm, and reasoning mode. We highlight our key insights: (i) Replacing stitched synthetic trajectories with real end-to-end tool-use trajectories yields a far stronger SFT initialization; high-diversity, model-aware datasets sustain exploration and markedly improve RL performance. (ii) Exploration-friendly techniques are crucial for agentic RL, such as clip higher, overlong reward shaping, and maintaining adequate policy entropy could improve the training efficiency. (iii) A deliberative strategy with fewer tool calls outperforms frequent tool calls or verbose self-reasoning, improving tool efficiency and final accuracy. Together, these simple practices consistently enhance agentic reasoning and training efficiency, achieving strong results on challenging benchmarks with smaller models, and establishing a practical baseline for future agentic RL research. Beyond these empirical insights, we further contribute a high-quality, real end-to-end agentic SFT dataset along with a high-quality RL dataset, and demonstrate the effectiveness of our insights in boosting the agentic reasoning ability of LLMs across four challenging benchmarks, including AIME2024/AIME2025, GPQA-Diamond, and LiveCodeBench-v6. With our recipes, 4B-sized models could also achieve superior agentic reasoning performance compared to 32B-sized models. Code and models: https://github.com/Gen-Verse/Open-AgentRL
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            <a href="https://www.alphaxiv.org/abs/2510.11695v1" target="_blank" rel="noopener noreferrer">
                当智能体交易时：面向LLM智能体的实时多市场交易基准
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            When Agents Trade: Live Multi-Market Trading Benchmark for LLM Agents
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Lingfei Qian, Xueqing Peng, Yan Wang, Vincent Jim Zhang, Huan He, Hanley Smith, ...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注LLM智能体在金融交易领域的基准测试，属于特定领域应用而非核心推荐系统、搜索或广告技术。虽然涉及LLM智能体，但交易基准与推荐/搜索/广告的排名、检索或用户建模问题缺乏直接关联，潜在应用场景不明确。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 17:54:09
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11695v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11695v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Although Large Language Model (LLM)-based agents are increasingly used in financial trading, it remains unclear whether they can reason and adapt in live markets, as most studies test models instead of agents, cover limited periods and assets, and rely on unverified data. To address these gaps, we introduce Agent Market Arena (AMA), the first lifelong, real-time benchmark for evaluating LLM-based trading agents across multiple markets. AMA integrates verified trading data, expert-checked news, and diverse agent architectures within a unified trading framework, enabling fair and continuous comparison under real conditions. It implements four agents, including InvestorAgent as a single-agent baseline, TradeAgent and HedgeFundAgent with different risk styles, and DeepFundAgent with memory-based reasoning, and evaluates them across GPT-4o, GPT-4.1, Claude-3.5-haiku, Claude-sonnet-4, and Gemini-2.0-flash. Live experiments on both cryptocurrency and stock markets demonstrate that agent frameworks display markedly distinct behavioral patterns, spanning from aggressive risk-taking to conservative decision-making, whereas model backbones contribute less to outcome variation. AMA thus establishes a foundation for rigorous, reproducible, and continuously evolving evaluation of financial reasoning and trading intelligence in LLM-based agents.
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            <a href="https://www.alphaxiv.org/abs/2510.11683v1" target="_blank" rel="noopener noreferrer">
                边界引导策略优化：面向扩散大语言模型的高效记忆强化学习
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            Boundary-Guided Policy Optimization for Memory-efficient RL of Diffusion Large Language Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Nianyi Lin, Jiajie Zhang, Lei Hou, Juanzi Li
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注扩散大语言模型的强化学习效率优化，属于纯粹的LLM技术范畴。虽然提到了强化学习和效率优化，但没有明确展示与推荐系统、搜索或广告的直接关联，且扩散模型在这些领域的应用尚不明确。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 17:47:50
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11683v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11683v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    A key challenge in applying reinforcement learning (RL) to diffusion large language models (dLLMs) lies in the intractability of their likelihood functions, which are essential for the RL objective, necessitating corresponding approximation in each training step. While existing methods approximate the log-likelihoods by their evidence lower bounds (ELBOs) via customized Monte Carlo (MC) sampling, the forward computational graphs of all MC samples need to be retained for the gradient computation of non-linear terms in the RL objective, resulting in significant memory overhead. This constraint restricts feasible sample sizes, leading to imprecise likelihood approximations and ultimately distorting the RL objective. To overcome this limitation, we propose \emph{Boundary-Guided Policy Optimization} (BGPO), a memory-efficient RL algorithm that maximizes a specially constructed lower bound of the ELBO-based objective. This lower bound is carefully designed to satisfy two key properties: (1) Linearity: it is formulated in a linear sum where each term depends only on a single MC sample, thereby enabling gradient accumulation across samples and ensuring constant memory usage; (2) Equivalence: Both the value and gradient of this lower bound are equal to those of the ELBO-based objective in on-policy training, making it also an effective approximation for the original RL objective. These properties allow BGPO to adopt a large MC sample size, resulting in more accurate likelihood approximations and improved RL objective estimation, which in turn leads to enhanced performance. Experiments show that BGPO significantly outperforms previous RL algorithms for dLLMs in math problem solving, code generation, and planning tasks.
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            <a href="https://www.alphaxiv.org/abs/2510.11652v1" target="_blank" rel="noopener noreferrer">
                ACADREASON：利用学术研究问题探索推理模型的极限
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            ACADREASON: Exploring the Limits of Reasoning Models with Academic Research Problems
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xin Gui, King Zhu, JinCheng Ren, Qianben Chen, Zekun Moore Wang, Yizhi LI, Xinpe...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注推理模型的基准测试和能力评估，这属于纯粹的NLP评估范畴，与推荐系统、搜索或广告的核心技术进展无关。虽然推理能力在理论上可能对某些搜索应用有帮助，但论文本身聚焦于学术推理问题的基准构建，缺乏明确的推荐/搜索/广告应用场景。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 17:30:36
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11652v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11652v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    In recent years, the research focus of large language models (LLMs) and agents has shifted increasingly from demonstrating novel capabilities to complex reasoning and tackling challenging tasks. However, existing evaluations focus mainly on math/code contests or general tasks, while existing multi-domain academic benchmarks lack sufficient reasoning depth, leaving the field without a rigorous benchmark for high-level reasoning. To fill this gap, we introduce the Acadreason benchmark, designed to evaluate the ability of LLMs and agents to acquire and reason over academic knowledge. It consists of 50 expert-annotated academic problems across five high-reasoning domains, including computer science, economics, law, mathematics, and philosophy. All questions are sourced from top-tier publications in recent years and undergo rigorous annotation and quality control to ensure they are both challenging and answerable. We conduct systematic evaluations of over 10 mainstream LLMs and agents. The results show that most LLMs scored below 20 points, with even the cutting-edge GPT-5 achieving only 16 points. While agents achieved higher scores, none exceeded 40 points. This demonstrates the current capability gap between LLMs and agents in super-intelligent academic research tasks and highlights the challenges of Acadreason.
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            <a href="https://www.alphaxiv.org/abs/2510.11618v1" target="_blank" rel="noopener noreferrer">
                StoryBox：基于大型语言模型的混合自底向上长文本故事生成的协作多智能体仿真
            </a>
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            StoryBox: Collaborative Multi-Agent Simulation for Hybrid Bottom-Up Long-Form Story Generation Using Large Language Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zehao Chen, Rong Pan, Haoran Li
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注使用LLM进行长文本故事生成的多智能体仿真，这属于纯粹的LLM内容生成应用。虽然涉及多智能体协作，但缺乏与推荐系统、搜索或广告领域的明确关联，属于AIGC和内容生成范畴，与当前关注点不匹配。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 16:57:32
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11618v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11618v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.MA</span></div>
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                    Human writers often begin their stories with an overarching mental scene, where they envision the interactions between characters and their environment. Inspired by this creative process, we propose a novel approach to long-form story generation, termed hybrid bottom-up long-form story generation, using multi-agent simulations. In our method, agents interact within a dynamic sandbox environment, where their behaviors and interactions with one another and the environment generate emergent events. These events form the foundation for the story, enabling organic character development and plot progression. Unlike traditional top-down approaches that impose rigid structures, our hybrid bottom-up approach allows for the natural unfolding of events, fostering more spontaneous and engaging storytelling. The system is capable of generating stories exceeding 10,000 words while maintaining coherence and consistency, addressing some of the key challenges faced by current story generation models. We achieve state-of-the-art performance across several metrics. This approach offers a scalable and innovative solution for creating dynamic, immersive long-form stories that evolve organically from agent-driven interactions.
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            <a href="https://www.alphaxiv.org/abs/2510.11584v1" target="_blank" rel="noopener noreferrer">
                LLMAtKGE：大型语言模型作为知识图谱嵌入的可解释攻击者
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            LLMAtKGE: Large Language Models as Explainable Attackers against Knowledge Graph Embeddings
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Ting Li, Yang Yang, Yipeng Yu, Liang Yao, Guoqing Chao, Ruifeng Xu
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注使用LLMs攻击知识图谱嵌入的对抗性攻击和可解释性，这属于安全/攻击领域，属于明确的无关主题。虽然提到了LLMs和知识图谱，但核心焦点是攻击方法而非在推荐/搜索/广告中的应用。知识图谱在推荐系统中可能有应用，但本文的攻击视角与当前关注的核心进展不相关。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 16:29:17
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11584v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11584v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    Adversarial attacks on knowledge graph embeddings (KGE) aim to disrupt the model's ability of link prediction by removing or inserting triples. A recent black-box method has attempted to incorporate textual and structural information to enhance attack performance. However, it is unable to generate human-readable explanations, and exhibits poor generalizability. In the past few years, large language models (LLMs) have demonstrated powerful capabilities in text comprehension, generation, and reasoning. In this paper, we propose LLMAtKGE, a novel LLM-based framework that selects attack targets and generates human-readable explanations. To provide the LLM with sufficient factual context under limited input constraints, we design a structured prompting scheme that explicitly formulates the attack as multiple-choice questions while incorporating KG factual evidence. To address the context-window limitation and hesitation issues, we introduce semantics-based and centrality-based filters, which compress the candidate set while preserving high recall of attack-relevant information. Furthermore, to efficiently integrate both semantic and structural information into the filter, we precompute high-order adjacency and fine-tune the LLM with a triple classification task to enhance filtering performance. Experiments on two widely used knowledge graph datasets demonstrate that our attack outperforms the strongest black-box baselines and provides explanations via reasoning, and showing competitive performance compared with white-box methods. Comprehensive ablation and case studies further validate its capability to generate explanations.
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            <a href="https://www.alphaxiv.org/abs/2510.11563v1" target="_blank" rel="noopener noreferrer">
                文化感知对话：面向大语言模型的框架与基准
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            Culturally-Aware Conversations: A Framework & Benchmark for LLMs
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Shreya Havaldar, Sunny Rai, Young-Min Cho, Lyle Ungar
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注文化感知对话的框架和基准构建，属于纯粹的LLM评估和对话系统范畴，与推荐系统、搜索或广告的核心技术进展缺乏直接关联。虽然文化感知在个性化推荐中可能有潜在应用，但论文标题明确聚焦于对话基准而非推荐/搜索/广告的具体技术方法。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 16:06:14
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11563v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11563v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    Existing benchmarks that measure cultural adaptation in LLMs are misaligned with the actual challenges these models face when interacting with users from diverse cultural backgrounds. In this work, we introduce the first framework and benchmark designed to evaluate LLMs in realistic, multicultural conversational settings. Grounded in sociocultural theory, our framework formalizes how linguistic style - a key element of cultural communication - is shaped by situational, relational, and cultural context. We construct a benchmark dataset based on this framework, annotated by culturally diverse raters, and propose a new set of desiderata for cross-cultural evaluation in NLP: conversational framing, stylistic sensitivity, and subjective correctness. We evaluate today's top LLMs on our benchmark and show that these models struggle with cultural adaptation in a conversational setting.
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            <a href="https://www.alphaxiv.org/abs/2510.11557v1" target="_blank" rel="noopener noreferrer">
                LLM宇宙中的隐形语言
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        <div class="mb-2 text-base text-gray-700">
            Invisible Languages of the LLM Universe
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Saurabh Khanna, Xinxu Li
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该标题暗示可能探讨LLM内部表征或新兴能力等基础LLM技术，这些技术可能通过改进语义理解或跨模态建模应用于推荐系统或搜索。然而，标题过于抽象和宽泛，缺乏具体的技术细节或明确的RecSys/Search/Ads应用指向，因此相关性较低。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 16:00:15
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11557v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11557v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    Large Language Models are trained on massive multilingual corpora, yet this abundance masks a profound crisis: of the world's 7,613 living languages, approximately 2,000 languages with millions of speakers remain effectively invisible in digital ecosystems. We propose a critical framework connecting empirical measurements of language vitality (real world demographic strength) and digitality (online presence) with postcolonial theory and epistemic injustice to explain why linguistic inequality in AI systems is not incidental but structural. Analyzing data across all documented human languages, we identify four categories: Strongholds (33%, high vitality and digitality), Digital Echoes (6%, high digitality despite declining vitality), Fading Voices (36%, low on both dimensions), and critically, Invisible Giants (27%, high vitality but near-zero digitality) - languages spoken by millions yet absent from the LLM universe. We demonstrate that these patterns reflect continuities from colonial-era linguistic hierarchies to contemporary AI development, constituting what we term digital epistemic injustice. Our analysis reveals that English dominance in AI is not a technical necessity but an artifact of power structures that systematically exclude marginalized linguistic knowledge. We conclude with implications for decolonizing language technology and democratizing access to AI benefits.
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            <a href="https://www.alphaxiv.org/abs/2510.11545v1" target="_blank" rel="noopener noreferrer">
                用于抗蒸馏的推理轨迹信息保留重构
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            Information-Preserving Reformulation of Reasoning Traces for Antidistillation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jiayu Ding, Lei Cui, Li Dong, Nanning Zheng, Furu Wei
        </div>
        
        
        
        
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文关注推理轨迹的重新表述和抗蒸馏技术，主要属于模型压缩和知识蒸馏领域。虽然蒸馏技术可能间接应用于推荐或搜索系统的模型部署优化，但论文标题未表明与推荐系统、搜索或广告的直接关联，也未涉及Transformer架构改进或LLM在推荐/搜索中的直接应用。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 15:42:11
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11545v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11545v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    Recent advances in Large Language Models (LLMs) show that extending the length of reasoning chains significantly improves performance on complex tasks. While revealing these reasoning traces helps users better follow, verify, and learn from the model's problem-solving process, it also makes them highly vulnerable to unauthorized distillation. To mitigate this risk, proprietary model providers often adopt aggressive protection strategies, such as replacing detailed reasoning with brief summaries, which deprive users of valuable intermediate information. To address this trade-off, we propose PART, an information-preserving antidistillation reformulation of reasoning traces. Motivated by the difference between how humans understand reasoning traces and how LLMs exploit them for supervised fine-tuning, we design a simple but effective two-step reformulation: removing self-talk behaviors and reordering sub-conclusions. A small auxiliary model is trained to perform this reformulation, incurring minimal computational overhead. Extensive experiments demonstrate that PART consistently disrupts distillation across student models of different sizes and types on various reasoning benchmarks. For instance, when training on reformulated traces, even the performance of a large 32B student model decreases from 54.17 to 46.88 on AIME 2024, corresponding to a 13.5% degradation.
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            <a href="https://www.alphaxiv.org/abs/2510.11537v1" target="_blank" rel="noopener noreferrer">
                集成编码器的PhoBERT与图注意力机制在越南语词级分类中的应用
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            An Encoder-Integrated PhoBERT with Graph Attention for Vietnamese Token-Level Classification
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Ba-Quang Nguyen
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于越南语特定语言模型的词级分类任务，属于特定语言的NLP应用。虽然涉及图注意力机制，但其核心是语言特定的分类问题，与推荐系统、搜索或广告的核心技术进展缺乏直接关联。该技术主要针对越南语处理，在通用推荐/搜索系统中的潜在应用非常有限。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 15:39:09
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11537v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11537v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    We propose a novel neural architecture named TextGraphFuseGAT, which integrates a pretrained transformer encoder (PhoBERT) with Graph Attention Networks for token-level classification tasks. The proposed model constructs a fully connected graph over the token embeddings produced by PhoBERT, enabling the GAT layer to capture rich inter-token dependencies beyond those modeled by sequential context alone. To further enhance contextualization, a Transformer-style self-attention layer is applied on top of the graph-enhanced embeddings. The final token representations are passed through a classification head to perform sequence labeling. We evaluate our approach on three Vietnamese benchmark datasets: PhoNER-COVID19 for named entity recognition in the COVID-19 domain, PhoDisfluency for speech disfluency detection, and VietMed-NER for medical-domain NER. VietMed-NER is the first Vietnamese medical spoken NER dataset, featuring 18 entity types collected from real-world medical speech transcripts and annotated with the BIO tagging scheme. Its specialized vocabulary and domain-specific expressions make it a challenging benchmark for token-level classification models. Experimental results show that our method consistently outperforms strong baselines, including transformer-only and hybrid neural models such as BiLSTM + CNN + CRF, confirming the effectiveness of combining pretrained semantic features with graph-based relational modeling for improved token classification across multiple domains.
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            <a href="https://www.alphaxiv.org/abs/2510.11529v1" target="_blank" rel="noopener noreferrer">
                基于大语言模型内部状态与结构化推理一致性的幻觉检测
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            Hallucination Detection via Internal States and Structured Reasoning Consistency in Large Language Models
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yusheng Song, Lirong Qiu, Xi Zhang, Zhihao Tang
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于LLM幻觉检测这一纯NLP中心主题，属于明确列出的无关主题范畴。虽然幻觉检测在通用LLM应用中很重要，但论文标题没有表明其在推荐系统、搜索或广告中的潜在应用价值，与当前关注的领域进展或使能技术缺乏直接关联。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 15:31:21
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                <a href="https://arxiv.org/abs/2510.11529v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11529v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    The detection of sophisticated hallucinations in Large Language Models (LLMs) is hampered by a ``Detection Dilemma'': methods probing internal states (Internal State Probing) excel at identifying factual inconsistencies but fail on logical fallacies, while those verifying externalized reasoning (Chain-of-Thought Verification) show the opposite behavior. This schism creates a task-dependent blind spot: Chain-of-Thought Verification fails on fact-intensive tasks like open-domain QA where reasoning is ungrounded, while Internal State Probing is ineffective on logic-intensive tasks like mathematical reasoning where models are confidently wrong. We resolve this with a unified framework that bridges this critical gap. However, unification is hindered by two fundamental challenges: the Signal Scarcity Barrier, as coarse symbolic reasoning chains lack signals directly comparable to fine-grained internal states, and the Representational Alignment Barrier, a deep-seated mismatch between their underlying semantic spaces. To overcome these, we introduce a multi-path reasoning mechanism to obtain more comparable, fine-grained signals, and a segment-aware temporalized cross-attention module to adaptively fuse these now-aligned representations, pinpointing subtle dissonances. Extensive experiments on three diverse benchmarks and two leading LLMs demonstrate that our framework consistently and significantly outperforms strong baselines. Our code is available: https://github.com/peach918/HalluDet.
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            <a href="https://www.alphaxiv.org/abs/2510.11498v1" target="_blank" rel="noopener noreferrer">
                ReLook：基于视觉的强化学习与多模态大语言模型评论器用于智能网页编码
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            ReLook: Vision-Grounded RL with a Multimodal LLM Critic for Agentic Web Coding
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yuhang Li, Chenchen Zhang, Ruilin Lv, Ao Liu, Ken Deng, Yuanxing Zhang, Jiaheng ...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注网页编码的智能体开发，结合视觉和强化学习技术，这与推荐系统、搜索或广告的核心领域相关性较低。虽然涉及多模态LLM技术，但其应用场景（网页编码）与RecSys/Search/Ads的直接应用或使能技术关联性较弱，缺乏明确的跨领域应用潜力。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 15:05:50
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                <a href="https://arxiv.org/abs/2510.11498v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11498v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.CL</span></div>
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                    While Large Language Models (LLMs) excel at algorithmic code generation, they struggle with front-end development, where correctness is judged on rendered pixels and interaction. We present ReLook, an agentic, vision-grounded reinforcement learning framework that empowers an agent to close a robust generate--diagnose--refine loop by invoking a multimodal LLM (MLLM) as a tool. During training, the agent uses the MLLM-in-the-loop both as a visual critic--scoring code with screenshots--and as a source of actionable, vision-grounded feedback; a strict zero-reward rule for invalid renders anchors renderability and prevents reward hacking. To prevent behavioral collapse, we introduce Forced Optimization, a strict acceptance rule that admits only improving revisions, yielding monotonically better trajectories. At inference, we decouple the critic and run a lightweight, critic-free self-edit cycle, keeping latency comparable to base decoding while retaining most of the gains. Across three widely used benchmarks, ReLook consistently outperforms strong baselines in vision-grounded front-end code generation, highlighting the benefits of agentic perception, visual rewards, and training-inference decoupling.
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            <a href="https://www.alphaxiv.org/abs/2510.11444v1" target="_blank" rel="noopener noreferrer">
                GenCNER：一种用于持续命名实体识别的生成式框架
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            GenCNER: A Generative Framework for Continual Named Entity Recognition
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yawen Yang, Fukun Ma, Shiao Meng, Aiwei Liu, Lijie Wen
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于命名实体识别（NLR）领域的持续学习问题，属于纯粹的NLP技术范畴。虽然采用了生成式方法，但其应用场景局限于实体识别任务，与推荐系统、搜索或广告中的核心排名、用户建模、多模态数据处理等关键问题缺乏直接关联，且未展示在RecSys/Search/Ads领域的潜在应用价值。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 14:15:31
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11444v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11444v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    Traditional named entity recognition (NER) aims to identify text mentions into pre-defined entity types. Continual Named Entity Recognition (CNER) is introduced since entity categories are continuously increasing in various real-world scenarios. However, existing continual learning (CL) methods for NER face challenges of catastrophic forgetting and semantic shift of non-entity type. In this paper, we propose GenCNER, a simple but effective Generative framework for CNER to mitigate the above drawbacks. Specifically, we skillfully convert the CNER task into sustained entity triplet sequence generation problem and utilize a powerful pre-trained seq2seq model to solve it. Additionally, we design a type-specific confidence-based pseudo labeling strategy along with knowledge distillation (KD) to preserve learned knowledge and alleviate the impact of label noise at the triplet level. Experimental results on two benchmark datasets show that our framework outperforms previous state-of-the-art methods in multiple CNER settings, and achieves the smallest gap compared with non-CL results.
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            <a href="https://www.alphaxiv.org/abs/2510.11434v1" target="_blank" rel="noopener noreferrer">
                你是谁，ChatGPT？大语言模型生成内容中的个性和人口统计风格
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            Who are you, ChatGPT? Personality and Demographic Style in LLM-Generated Content
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Dana Sotto Porat, Ella Rabinovich
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要研究LLM生成内容中的个性和人口统计特征，这属于纯粹的LLM中心化主题，与推荐系统、搜索或广告的核心技术进展无关。虽然LLM个性分析可能间接影响用户体验，但论文焦点是内容生成特性而非实际的推荐、搜索或广告应用。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 14:06:17
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11434v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11434v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    Generative large language models (LLMs) have become central to everyday life, producing human-like text across diverse domains. A growing body of research investigates whether these models also exhibit personality- and demographic-like characteristics in their language. In this work, we introduce a novel, data-driven methodology for assessing LLM personality without relying on self-report questionnaires, applying instead automatic personality and gender classifiers to model replies on open-ended questions collected from Reddit. Comparing six widely used models to human-authored responses, we find that LLMs systematically express higher Agreeableness and lower Neuroticism, reflecting cooperative and stable conversational tendencies. Gendered language patterns in model text broadly resemble those of human writers, though with reduced variation, echoing prior findings on automated agents. We contribute a new dataset of human and model responses, along with large-scale comparative analyses, shedding new light on the topic of personality and demographic patterns of generative AI.
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            <a href="https://www.alphaxiv.org/abs/2510.11423v1" target="_blank" rel="noopener noreferrer">
                超越群体：基于大语言模型增强的社区笔记用于治理健康错误信息
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            Beyond the Crowd: LLM-Augmented Community Notes for Governing Health Misinformation
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jiaying Wu, Zihang Fu, Haonan Wang, Fanxiao Li, Min-Yen Kan
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注健康错误信息治理这一特定领域应用，属于医疗/健康领域的垂直应用。虽然涉及LLM技术，但核心应用场景（健康信息治理）与推荐系统、搜索或广告的核心技术进展无关，且健康领域属于明确的无关主题范畴。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 13:57:23
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11423v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11423v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.SI</span><span class="category-tag">cs.CL</span></div>
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                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Community Notes, the crowd-sourced misinformation governance system on X (formerly Twitter), enables users to flag misleading posts, attach contextual notes, and vote on their helpfulness. However, our analysis of 30.8K health-related notes reveals significant latency, with a median delay of 17.6 hours before the first note receives a helpfulness status. To improve responsiveness during real-world misinformation surges, we propose CrowdNotes+, a unified framework that leverages large language models (LLMs) to augment Community Notes for faster and more reliable health misinformation governance. CrowdNotes+ integrates two complementary modes: (1) evidence-grounded note augmentation and (2) utility-guided note automation, along with a hierarchical three-step evaluation that progressively assesses relevance, correctness, and helpfulness. We instantiate the framework through HealthNotes, a benchmark of 1.2K helpfulness-annotated health notes paired with a fine-tuned helpfulness judge. Experiments on fifteen LLMs reveal an overlooked loophole in current helpfulness evaluation, where stylistic fluency is mistaken for factual accuracy, and demonstrate that our hierarchical evaluation and LLM-augmented generation jointly enhance factual precision and evidence utility. These results point toward a hybrid human-AI governance model that improves both the rigor and timeliness of crowd-sourced fact-checking.
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            <a href="https://www.alphaxiv.org/abs/2510.11408v1" target="_blank" rel="noopener noreferrer">
                基于有限人类数据的有效调查模拟：提示、微调和校正的作用
            </a>
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        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Valid Survey Simulations with Limited Human Data: The Roles of Prompting, Fine-Tuning, and Rectification
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Stefan Krsteski, Giuseppe Russo, Serina Chang, Robert West, Kristina Gligorić
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注调查模拟和有限人类数据场景，这属于数据收集和验证的通用方法学范畴。虽然提到了提示和微调等LLM技术，但核心应用场景（调查模拟）与推荐系统、搜索或广告的排名和建模问题没有直接关联，潜在应用不明确。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 13:48:07
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11408v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11408v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    Surveys provide valuable insights into public opinion and behavior, but their execution is costly and slow. Large language models (LLMs) have been proposed as a scalable, low-cost substitute for human respondents, but their outputs are often biased and yield invalid estimates. We study the interplay between synthesis methods that use LLMs to generate survey responses and rectification methods that debias population estimates, and explore how human responses are best allocated between them. Using two panel surveys with questions on nutrition, politics, and economics, we find that synthesis alone introduces substantial bias (24-86%), whereas combining it with rectification reduces bias below 5% and increases effective sample size by up to 14%. Overall, we challenge the common practice of using all human responses for fine-tuning, showing that under a fixed budget, allocating most to rectification results in far more effective estimation.
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            <a href="https://www.alphaxiv.org/abs/2510.11391v1" target="_blank" rel="noopener noreferrer">
                DocReward：一种用于结构化和风格化的文档奖励模型
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            <i class="fa fa-star mr-1"></i>2/10
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            DocReward: A Document Reward Model for Structuring and Stylizing
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Junpeng Liu, Yuzhong Zhao, Bowen Cao, Jiayu Ding, Yilin Jia, Tengchao Lv, Yupan ...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注文档结构化和风格化的奖励模型，这属于内容生成和格式化的范畴。虽然奖励模型技术本身可能对推荐系统或搜索有一定启发，但论文的核心应用场景（文档结构化和风格化）与我的核心关注领域（推荐系统、搜索、广告中的排名和建模）关联度较低，属于AIGC和内容生成的范畴。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 13:36:32
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11391v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11391v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Recent advances in agentic workflows have enabled the automation of tasks such as professional document generation. However, they primarily focus on textual quality, neglecting visual structure and style, which are crucial for readability and engagement. This gap arises mainly from the absence of suitable reward models to guide agentic workflows toward producing documents with stronger structural and stylistic quality. To address this, we propose DocReward, a document reward model that evaluates documents based on their structure and style. We construct a multi-domain dataset DocPair of 117K paired documents, covering 32 domains and 267 document types, each including a high- and low-professionalism document with identical content but different structure and style. This enables the model to evaluate professionalism comprehensively, and in a textual-quality-agnostic way. DocReward is trained using the Bradley-Terry loss to score documents, penalizing predictions that contradict the annotated ranking. To assess the performance of reward models, we create a test dataset containing document bundles ranked by well-educated human evaluators. Notably, DocReward outperforms GPT-4o and GPT-5 in accuracy by 30.6 and 19.4 percentage points, respectively, demonstrating its superiority over baselines. In an extrinsic evaluation of document generation, DocReward achieves a significantly higher win rate of 60.8%, compared to GPT-5's 37.7% win rate, demonstrating its utility in guiding generation agents toward producing human-preferred documents.
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            <a href="https://www.alphaxiv.org/abs/2510.11389v1" target="_blank" rel="noopener noreferrer">
                超越生存：基于人类对齐策略在社交推理游戏中评估大语言模型
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            Beyond Survival: Evaluating LLMs in Social Deduction Games with Human-Aligned Strategies
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zirui Song, Yuan Huang, Junchang Liu, Haozhe Luo, Chenxi Wang, Lang Gao, Zixiang...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注LLM在社交推理游戏中的评估和人类对齐策略，这属于纯粹的LLM评估和游戏应用场景。虽然涉及人类对齐，但缺乏与推荐系统、搜索或广告领域的明确关联，也没有展示在异构数据处理或Transformer架构效率方面的潜在应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 13:33:30
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11389v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11389v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Social deduction games like Werewolf combine language, reasoning, and strategy, providing a testbed for studying natural language and social intelligence. However, most studies reduce the game to LLM-based self-play, yielding templated utterances and anecdotal cases that overlook the richness of social gameplay. Evaluation further relies on coarse metrics such as survival time or subjective scoring due to the lack of quality reference data. To address these gaps, we curate a high-quality, human-verified multimodal Werewolf dataset containing over 100 hours of video, 32.4M utterance tokens, and 15 rule variants. Based on this dataset, we propose a novel strategy-alignment evaluation that leverages the winning faction's strategies as ground truth in two stages: 1) Speech evaluation, formulated as multiple-choice-style tasks that assess whether the model can adopt appropriate stances across five dimensions of social ability; and 2) Decision evaluation, which assesses the model's voting choices and opponent-role inferences. This framework enables a fine-grained evaluation of models' linguistic and reasoning capabilities, while capturing their ability to generate strategically coherent gameplay. Our experiments show that state-of-the-art LLMs show diverse performance, with roughly half remain below 0.50, revealing clear gaps in deception and counterfactual reasoning. We hope our dataset further inspires research on language, reasoning, and strategy in multi-agent interaction.
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            <a href="https://www.alphaxiv.org/abs/2510.11370v1" target="_blank" rel="noopener noreferrer">
                通过对齐训练和推理路由器来稳定混合专家强化学习
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            Stabilizing MoE Reinforcement Learning by Aligning Training and Inference Routers
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Wenhan Ma, Hailin Zhang, Liang Zhao, Yifan Song, Yudong Wang, Zhifang Sui, Fuli ...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文虽然涉及混合专家（MoE）架构，属于Transformer技术范畴，但核心聚焦于强化学习（RL）的稳定性问题。论文没有明确展示与推荐系统、搜索或广告的直接相关性，且强化学习应用不明确，因此相关性较低。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 13:11:27
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11370v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11370v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.LG</span></div>
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                    Reinforcement learning (RL) has emerged as a crucial approach for enhancing the capabilities of large language models. However, in Mixture-of-Experts (MoE) models, the routing mechanism often introduces instability, even leading to catastrophic RL training collapse. We analyze the training-inference consistency of MoE models and identify a notable discrepancy in routing behaviors between the two phases. Moreover, even under identical conditions, the routing framework can yield divergent expert selections across repeated forward passes. To address this foundational inconsistency, we propose Rollout Routing Replay (R3), a method that records routing distributions from the inference engine and replays them during training. R3 significantly reduces training-inference policy KL divergence and mitigates extreme discrepancies without compromising training speed. Extensive experiments on various settings confirm that R3 succeeds in stabilizing RL training, preventing collapse and outperforming methods such as GSPO and TIS. We believe this work can offer a new solution for stabilizing RL in MoE models.
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            <a href="https://www.alphaxiv.org/abs/2510.11330v1" target="_blank" rel="noopener noreferrer">
                Diffusion-Link：用于弥合音频-文本模态差距的扩散概率模型
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            Diffusion-Link: Diffusion Probabilistic Model for Bridging the Audio-Text Modality Gap
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>KiHyun Nam, Jongmin Choi, Hyeongkeun Lee, Jungwoo Heo, Joon Son Chung
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于音频-文本模态对齐，属于跨模态学习范畴，与推荐系统、搜索或广告的核心技术关联较弱。虽然扩散模型技术本身具有潜力，但论文的应用场景（音频-文本）与异构数据处理（如用户序列和上下文特征）的直接关联性不足，难以直接应用于当前关注的领域。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 12:25:33
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11330v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11330v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.SD</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span><span class="category-tag">cs.LG</span><span class="category-tag">eess.AS</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Contrastive audio-language pretraining yields powerful joint representations, yet a persistent audio-text modality gap limits the benefits of coupling multimodal encoders with large language models (LLMs). We present Diffusion-Link, a diffusion-based modality-bridging module that generatively maps audio embeddings into the text-embedding distribution. The module is trained at the output embedding from the frozen multimodal encoder and implemented as a lightweight network with three residual MLP blocks. To assess the effect of Diffusion-Link on multimodal encoder-LLM coupling, we evaluate on Automatic Audio Captioning (AAC); to our knowledge, this is the first application of diffusion-based modality bridging to AAC. We report two results. (1) Modality-gap analysis: on similarity and geometric criteria, Diffusion-Link reduces the modality gap the most among prior diffusion-based methods and shows a collective migration of audio embeddings toward the text distribution. (2) Downstream AAC: attaching Diffusion-Link to the same multimodal LLM baseline achieves state-of-the-art on AudioCaps in both zero-shot and fully supervised captioning without external knowledge, with relative gains up to 52.5% and 7.5%, respectively. These findings show that closing the modality gap is pivotal for effective coupling between multimodal encoders and LLMs, and diffusion-based modality bridging offers a promising direction beyond knowledge-retrieval-centric designs. Code will be released upon acceptance https://github.com/DevKiHyun/Diffusion-Link
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                基于模板的文本到图像对齐用于语言可访问性：文本简化可视化的研究
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            Template-Based Text-to-Image Alignment for Language Accessibility: A Study on Visualizing Text Simplifications
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Belkiss Souayed, Sarah Ebling, Yingqiang Gao
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注文本到图像生成和语言可访问性，属于AIGC和内容生成范畴，与RecSys/Search/Ads的核心排名和推荐任务关联度较低。虽然文本简化技术可能间接影响搜索内容的可访问性，但这并非论文的核心技术贡献方向，且缺乏明确的推荐或搜索应用场景。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 12:03:36
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11314v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11314v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Individuals with intellectual disabilities often have difficulties in comprehending complex texts. While many text-to-image models prioritize aesthetics over accessibility, it is not clear how visual illustrations relate to text simplifications (TS) generated from them. This paper presents a structured vision-language model (VLM) prompting framework for generating accessible images from simplified texts. We designed five prompt templates, i.e., Basic Object Focus, Contextual Scene, Educational Layout, Multi-Level Detail, and Grid Layout, each following distinct spatial arrangements while adhering to accessibility constraints such as object count limits, spatial separation, and content restrictions. Using 400 sentence-level simplifications from four established TS datasets (OneStopEnglish, SimPA, Wikipedia, and ASSET), we conducted a two-phase evaluation: Phase 1 assessed prompt template effectiveness with CLIPScores, and Phase 2 involved human annotation of generated images across ten visual styles by four accessibility experts. Results show that the Basic Object Focus prompt template achieved the highest semantic alignment, indicating that visual minimalism enhances language accessibility. Expert evaluation further identified Retro style as the most accessible and Wikipedia as the most effective data source. Inter-annotator agreement varied across dimensions, with Text Simplicity showing strong reliability and Image Quality proving more subjective. Overall, our framework offers practical guidelines for accessible content generation and underscores the importance of structured prompting in AI-generated visual accessibility tools.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.11307v1" target="_blank" rel="noopener noreferrer">
                FOSSIL：利用次优样本反馈实现数据高效泛化的模仿学习，用于具身视觉与语言任务
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            <i class="fa fa-star mr-1"></i>2/10
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            FOSSIL: Harnessing Feedback on Suboptimal Samples for Data-Efficient Generalisation with Imitation Learning for Embodied Vision-and-Language Tasks
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Sabrina McCallum, Amit Parekh, Alessandro Suglia
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注具身AI中的视觉语言任务和模仿学习，这与推荐系统、搜索或广告的核心领域关联度较低。虽然涉及多模态学习概念，但其在具身环境中的具体应用场景难以直接迁移到RecSys/Search/Ads领域，且缺乏明确的跨领域应用潜力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 11:55:21
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11307v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11307v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                    Current approaches to embodied AI tend to learn policies from expert demonstrations. However, without a mechanism to evaluate the quality of demonstrated actions, they are limited to learning from optimal behaviour, or they risk replicating errors and inefficiencies. While reinforcement learning offers one alternative, the associated exploration typically results in sacrificing data efficiency. This work explores how agents trained with imitation learning can learn robust representations from both optimal and suboptimal demonstrations when given access to constructive language feedback as a means to contextualise different modes of behaviour. We directly provide language feedback embeddings as part of the input sequence into a Transformer-based policy, and optionally complement the traditional next action prediction objective with auxiliary self-supervised learning objectives for feedback prediction. We test our approach on a range of embodied Vision-and-Language tasks in our custom BabyAI-XGen environment and show significant improvements in agents' compositional generalisation abilities and robustness, suggesting that our data-efficient method allows models to successfully convert suboptimal behaviour into learning opportunities. Overall, our results suggest that language feedback is a competitive and intuitive alternative to intermediate scalar rewards for language-specified embodied tasks.
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            <a href="https://www.alphaxiv.org/abs/2510.11288v1" target="_blank" rel="noopener noreferrer">
                通过上下文学习出现的错位：狭窄的上下文示例可能产生广泛错位的大型语言模型
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Emergent Misalignment via In-Context Learning: Narrow in-context examples can produce broadly misaligned LLMs
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Nikita Afonin, Nikita Andriyanov, Nikhil Bageshpura, Kyle Liu, Kevin Zhu, Sunish...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注LLM的对齐问题和上下文学习中的错位现象，这属于纯粹的LLM中心话题。虽然LLM对齐在理论上可能影响推荐/搜索系统的可靠性，但论文没有明确展示与推荐系统、搜索或广告的直接应用关联，主要停留在NLP评估和基准测试层面。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 11:23:56
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11288v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11288v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Recent work has shown that narrow finetuning can produce broadly misaligned LLMs, a phenomenon termed emergent misalignment (EM). While concerning, these findings were limited to finetuning and activation steering, leaving out in-context learning (ICL). We therefore ask: does EM emerge in ICL? We find that it does: across three datasets, three frontier models produce broadly misaligned responses at rates between 2% and 17% given 64 narrow in-context examples, and up to 58% with 256 examples. We also examine mechanisms of EM by eliciting step-by-step reasoning (while leaving in-context examples unchanged). Manual analysis of the resulting chain-of-thought shows that 67.5% of misaligned traces explicitly rationalize harmful outputs by adopting a reckless or dangerous ''persona'', echoing prior results on finetuning-induced EM.
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            <a href="https://www.alphaxiv.org/abs/2510.11225v1" target="_blank" rel="noopener noreferrer">
                基于定理证明的神经语义解析评估
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            A Theorem-Proving-Based Evaluation of Neural Semantic Parsing
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Hayate Funakura, Hyunsoo Kim, Koji Mineshima
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文关注神经语义解析的评估方法，属于纯粹的NLP评估基准范畴，与我的核心关注点无关。虽然语义解析在理论上可能与搜索相关，但论文重点在于定理证明的评估方法，没有展示在推荐系统、搜索或广告中的直接应用潜力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 10:09:38
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11225v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11225v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Graph-matching metrics such as Smatch are the de facto standard for evaluating neural semantic parsers, yet they capture surface overlap rather than logical equivalence. We reassess evaluation by pairing graph-matching with automated theorem proving. We compare two approaches to building parsers: supervised fine-tuning (T5-Small/Base) and few-shot in-context learning (GPT-4o/4.1/5), under normalized and unnormalized targets. We evaluate outputs using graph-matching, bidirectional entailment between source and target formulas with a first-order logic theorem prover, and well-formedness. Across settings, we find that models performing well on graph-matching often fail to produce logically equivalent formulas. Normalization reduces incidental target variability, improves well-formedness, and strengthens logical adequacy. Error analysis shows performance degrades with increasing formula complexity and with coordination, prepositional phrases, and passive voice; the dominant failures involve variable binding and indexing, and predicate naming. These findings highlight limits of graph-based metrics for reasoning-oriented applications and motivate logic-sensitive evaluation and training objectives together with simplified, normalized target representations. All code and data for our experiments are publicly available.
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            <a href="https://www.alphaxiv.org/abs/2510.11218v1" target="_blank" rel="noopener noreferrer">
                LLM短答案与长答案之间事实对齐的奇特案例
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        <div class="mb-2 text-base text-gray-700">
            The Curious Case of Factual (Mis)Alignment between LLMs' Short- and Long-Form Answers
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Saad Obaid ul Islam, Anne Lauscher, Goran Glavaš
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要研究LLM在不同长度回答中的事实一致性，这属于幻觉和评估基准等纯NLP中心主题，与我的核心关注点无关。虽然事实对齐在理论上可能影响推荐系统的可信度，但论文没有明确展示在推荐、搜索或广告中的实际应用潜力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 10:00:58
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11218v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11218v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                    Large language models (LLMs) can correctly answer "When was Einstein born?" yet fail to provide the same date when writing about Einstein's life revealing a fundamental inconsistency in how models access factual knowledge across task complexities. While models display impressive accuracy on factual question-answering benchmarks, the reliability gap between simple and complex queries remains poorly understood, eroding their trustworthiness. In this work, we introduce Short-Long Form Alignment for Factual Question Answering (SLAQ), a controlled evaluation framework that compares LLMs' answers to the same factual questions asked (a) in isolation (short) vs. (b) integrated into complex queries (long). Looking at 16 LLMs across 600 queries, we find a systematic misalignment of answers to the corresponding short and long queries. We further uncover position-dependent accuracy loss and momentum effects where consecutive correct or incorrect answers create self-reinforcing patterns. Through mechanistic analysis, we find that aligned facts activate overlapping model internals, and that metrics based on mechanistic similarity can predict short-long answer alignment with up to 78% accuracy. Our work establishes factual consistency over query complexity as an important aspect of LLMs' trustworthiness and challenges current evaluation practices, which implicitly assume that good performance for simple factual queries implies reliability in more complex knowledge-seeking tasks too.
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            <a href="https://www.alphaxiv.org/abs/2510.11210v1" target="_blank" rel="noopener noreferrer">
                话语回路：语言模型如何理解话语关系？
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            Discursive Circuits: How Do Language Models Understand Discourse Relations?
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yisong Miao, Min-Yen Kan
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要研究语言模型对话语关系的理解能力，这属于NLP基础研究范畴，与推荐系统、搜索或广告的核心技术关联度较低。虽然话语理解在理论上可能对搜索中的长文档理解有一定帮助，但这种应用潜力非常间接且不明确，不符合当前关注的核心技术方向。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 09:45:49
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11210v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11210v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.LG</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Which components in transformer language models are responsible for discourse understanding? We hypothesize that sparse computational graphs, termed as discursive circuits, control how models process discourse relations. Unlike simpler tasks, discourse relations involve longer spans and complex reasoning. To make circuit discovery feasible, we introduce a task called Completion under Discourse Relation (CuDR), where a model completes a discourse given a specified relation. To support this task, we construct a corpus of minimal contrastive pairs tailored for activation patching in circuit discovery. Experiments show that sparse circuits ($\approx 0.2\%$ of a full GPT-2 model) recover discourse understanding in the English PDTB-based CuDR task. These circuits generalize well to unseen discourse frameworks such as RST and SDRT. Further analysis shows lower layers capture linguistic features such as lexical semantics and coreference, while upper layers encode discourse-level abstractions. Feature utility is consistent across frameworks (e.g., coreference supports Expansion-like relations).
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                工具集成强化学习能否在不同领域间实现泛化？
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            Can Tool-Integrated Reinforcement Learning Generalize Across Diverse Domains?
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zhengyu Chen, Jinluan Yang, Teng Xiao, Ruochen Zhou, Luan Zhang, Xiangyu Xi, Xia...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文关注强化学习与工具集成的泛化能力，属于强化学习领域而非推荐系统、搜索或广告的核心技术。虽然工具集成可能涉及外部知识调用，但论文重点在于跨领域泛化而非在推荐/搜索/广告中的具体应用，与当前关注的LLM技术、Transformer架构或直接应用相关性较低。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 09:19:13
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11184v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11184v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Recent advances in large language models (LLMs) have demonstrated remarkable capabilities in reasoning and tool utilization. However, the generalization of tool-augmented reinforcement learning (RL) across diverse domains remains underexplored. In this work, we investigate the cross-domain generalization of an LLM agent equipped with a code interpreter tool, which is exclusively trained on mathematical problem-solving tasks. Despite the restricted training domain, we evaluate the agent's performance across several distinct reasoning domains. The results reveal that RL-based tool usage learned from mathematical tasks can be effectively transferred to complex tasks in other domains, enabling great task performance and high token efficiency. To facilitate this cross-domain transfer, we propose a Tool Generalization Reinforcement Learning (TGRL) framework designed to promote domain-agnostic learning and skill migration, encompassing: (i) a standardized tool interface that abstracts domain-specific nuances through consistent formatting and explicit termination, fostering transferable invocation patterns; (ii) a dual-component reward system that decomposes rewards to incentivize generalizable behaviors like tool efficiency and reasoning abstraction, ensuring alignment and robustness across domain shifts; and (iii) an XML-based prompt template that separates thinking, tool calls, and responses to encourage modular, domain-invariant planning and coherent multi-turn interactions. Extensive experiments across diverse benchmarks validate our approach, achieving state-of-the-art performance and highlighting the cross-domain potential of Tool RL for LLM reasoning.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.11151v1" target="_blank" rel="noopener noreferrer">
                TypePilot：利用Scala类型系统保障LLM生成代码的安全性
            </a>
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            TypePilot: Leveraging the Scala Type System for Secure LLM-generated Code
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Alexander Sternfeld, Andrei Kucharavy, Ljiljana Dolamic
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注LLM生成代码的安全性保障，属于LLM安全性和代码生成领域。虽然涉及LLM技术，但其应用场景（代码安全）与推荐系统、搜索或广告的核心技术栈没有直接关联，且安全主题属于明确排除的无关话题范畴。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 08:44:01
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11151v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11151v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.CR</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Large language Models (LLMs) have shown remarkable proficiency in code generation tasks across various programming languages. However, their outputs often contain subtle but critical vulnerabilities, posing significant risks when deployed in security-sensitive or mission-critical systems. This paper introduces TypePilot, an agentic AI framework designed to enhance the security and robustness of LLM-generated code by leveraging strongly typed and verifiable languages, using Scala as a representative example. We evaluate the effectiveness of our approach in two settings: formal verification with the Stainless framework and general-purpose secure code generation. Our experiments with leading open-source LLMs reveal that while direct code generation often fails to enforce safety constraints, just as naive prompting for more secure code, our type-focused agentic pipeline substantially mitigates input validation and injection vulnerabilities. The results demonstrate the potential of structured, type-guided LLM workflows to improve the SotA of the trustworthiness of automated code generation in high-assurance domains.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.11144v1" target="_blank" rel="noopener noreferrer">
                How²：如何从程序性How-to问题中学习
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            <i class="fa fa-star mr-1"></i>2/10
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            $How^{2}$: How to learn from procedural How-to questions
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Gautier Dagan, Frank Keller, Alex Lascarides
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文关注程序性How-to问题的学习，这主要属于问答系统和知识获取领域。虽然程序性知识在搜索系统中可能有潜在应用（如回答用户的操作指导类问题），但该研究更偏向于通用的问答能力，与推荐系统、广告或核心的Transformer架构进展关联较弱。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 08:35:20
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11144v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11144v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span></div>
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            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    An agent facing a planning problem can use answers to how-to questions to reduce uncertainty and fill knowledge gaps, helping it solve both current and future tasks. However, their open ended nature, where valid answers to "How do I X?" range from executable actions to high-level descriptions of X's sub-goals, makes them challenging for AI agents to ask, and for AI experts to answer, in ways that support efficient planning. We introduce $How^{2}$, a memory agent framework that enables agents to ask how-to questions, store the answers, and reuse them for lifelong learning in interactive environments. We evaluate our approach in Plancraft, a Minecraft crafting environment, where agents must complete an assembly task by manipulating inventory items. Using teacher models that answer at varying levels of abstraction, from executable action sequences to high-level subgoal descriptions, we show that lifelong learning agents benefit most from answers that are abstracted and decoupled from the current state. $How^{2}$ offers a way for LLM-based agents to improve their planning capabilities over time by asking questions in interactive environments.
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            <a href="https://www.alphaxiv.org/abs/2510.11004v1" target="_blank" rel="noopener noreferrer">
                使用大型语言模型智能体自动化结构工程工作流程
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            Automating Structural Engineering Workflows with Large Language Model Agents
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Haoran Liang, Yufa Zhou, Mohammad Talebi Kalaleh, Qipei Mei
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于结构工程领域的特定应用，属于土木工程专业领域，与推荐系统、搜索或广告的核心关注点无关。虽然涉及LLM技术，但其应用场景过于特定且与目标领域没有明显关联，无法为RecSys/Search/Ads提供直接价值或技术启发。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 04:38:46
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11004v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11004v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.MA</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CE</span><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    We introduce $\textbf{MASSE}$, the first Multi-Agent System for Structural Engineering, effectively integrating large language model (LLM)-based agents with real-world engineering workflows. Structural engineering is a fundamental yet traditionally stagnant domain, with core workflows remaining largely unchanged for decades despite its substantial economic impact and global market size. Recent advancements in LLMs have significantly enhanced their ability to perform complex reasoning, long-horizon planning, and precise tool utilization -- capabilities well aligned with structural engineering tasks such as interpreting design codes, executing load calculations, and verifying structural capacities. We present a proof-of-concept showing that most real-world structural engineering workflows can be fully automated through a training-free LLM-based multi-agent system. MASSE enables immediate deployment in professional environments, and our comprehensive validation on real-world case studies demonstrates that it can reduce expert workload from approximately two hours to mere minutes, while enhancing both reliability and accuracy in practical engineering scenarios.
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            <a href="https://www.alphaxiv.org/abs/2510.10971v1" target="_blank" rel="noopener noreferrer">
                RV-HATE：基于强化多模块投票的隐式仇恨言论检测
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            RV-HATE: Reinforced Multi-Module Voting for Implicit Hate Speech Detection
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yejin Lee, Hyeseon Ahn, Yo-Sub Han
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注仇恨言论检测这一内容安全领域，属于内容审核而非推荐系统、搜索或广告的核心技术。虽然涉及多模态和强化学习，但其应用场景（内容安全）与用户建模、个性化推荐、广告排名等核心业务领域关联性较弱，不符合当前关注的任一技术方向。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 03:21:51
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.10971v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.10971v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span><span class="category-tag">68T50</span><span class="category-tag">I.2.7</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Hate speech remains prevalent in human society and continues to evolve in its forms and expressions. Modern advancements in internet and online anonymity accelerate its rapid spread and complicate its detection. However, hate speech datasets exhibit diverse characteristics primarily because they are constructed from different sources and platforms, each reflecting different linguistic styles and social contexts. Despite this diversity, prior studies on hate speech detection often rely on fixed methodologies without adapting to data-specific features. We introduce RV-HATE, a detection framework designed to account for the dataset-specific characteristics of each hate speech dataset. RV-HATE consists of multiple specialized modules, where each module focuses on distinct linguistic or contextual features of hate speech. The framework employs reinforcement learning to optimize weights that determine the contribution of each module for a given dataset. A voting mechanism then aggregates the module outputs to produce the final decision. RV-HATE offers two primary advantages: (1)~it improves detection accuracy by tailoring the detection process to dataset-specific attributes, and (2)~it also provides interpretable insights into the distinctive features of each dataset. Consequently, our approach effectively addresses implicit hate speech and achieves superior performance compared to conventional static methods. Our code is available at https://github.com/leeyejin1231/RV-HATE.
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            <a href="https://www.alphaxiv.org/abs/2510.10965v1" target="_blank" rel="noopener noreferrer">
                先判断再回答：多模态大语言模型能否识别问题中的错误前提？
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            Judge Before Answer: Can MLLM Discern the False Premise in Question?
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jidong Li, Lingyong Fang, Haodong Zhao, Sufeng Duan, Gongshen Liu
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注多模态大语言模型(MLLM)的推理能力和错误前提识别，属于纯粹的模型能力评估范畴。虽然涉及LLM技术，但论文焦点是模型在理解任务中的表现评估，而非在推荐系统、搜索或广告领域的实际应用或架构改进，与当前关注的技术应用方向关联度较低。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 03:17:00
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.10965v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.10965v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Multimodal large language models (MLLMs) have witnessed astonishing advancements in recent years. Despite these successes, MLLMs remain vulnerable to flase premise problems. However, existing benchmarks targeting this issue are limited in scope: they often lack fine-grained categorization, exhibit insufficient coverage, and thus fail to provide a rigorous evaluation of the ability of models to recognize false premises. To bridge this gap, we introduce a fully automated pipeline for constructing a comprehensive benchmark of false premise questions. Our method systematically categorizes the premises into three main types and thirteen subtypes according to the abilities required to identify the premises, resulting in the JBA dataset.Results show current MLLMs still struggle with false premise recognition. Building upon this benchmark, we further propose a recognition enhancement framework tailored to strengthen the robustness of MLLMs to detect false premises. Extensive experiments demonstrate that models trained with our framework achieve significant improvements in false premise recognition.
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                重新发现熵正则化：自适应系数释放其在大型语言模型强化学习中的潜力
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            Rediscovering Entropy Regularization: Adaptive Coefficient Unlocks Its Potential for LLM Reinforcement Learning
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xiaoyun Zhang, Xiaojian Yuan, Di Huang, Wang You, Chen Hu, Jingqing Ruan, Kejian...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注强化学习中的熵正则化技术，属于纯粹的强化学习优化方法。虽然标题提到LLM，但内容聚焦于RL训练技术本身，没有明确展示在推荐系统、搜索或广告中的潜在应用。该技术可能间接影响RLHF训练，但缺乏直接的应用场景说明。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 03:10:26
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.10959v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.10959v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span><span class="category-tag">stat.ML</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Reasoning ability has become a defining capability of Large Language Models (LLMs), with Reinforcement Learning with Verifiable Rewards (RLVR) emerging as a key paradigm to enhance it. However, RLVR training often suffers from policy entropy collapse, where the policy becomes overly deterministic, hindering exploration and limiting reasoning performance. While entropy regularization is a common remedy, its effectiveness is highly sensitive to the fixed coefficient, making it unstable across tasks and models. In this work, we revisit entropy regularization in RLVR and argue that its potential has been largely underestimated. Our analysis shows that (i) tasks of varying difficulty demand distinct exploration intensities, and (ii) balanced exploration may require the policy entropy to be maintained within a moderate range below its initial level. Therefore, we propose Adaptive Entropy Regularization (AER)--a framework that dynamically balances exploration and exploitation via three components: difficulty-aware coefficient allocation, initial-anchored target entropy, and dynamic global coefficient adjustment. Experiments on multiple mathematical reasoning benchmarks show that AER consistently outperforms baselines, improving both reasoning accuracy and exploration capability.
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            <a href="https://www.alphaxiv.org/abs/2510.10936v1" target="_blank" rel="noopener noreferrer">
                基于双向LSTM-CNNs-CRF的端到端序列标注：可复现性研究
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            <i class="fa fa-star mr-1"></i>2/10
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            End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF: A Reproducibility Study
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Anirudh Ganesh, Jayavardhan Reddy
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要研究序列标注任务的经典神经网络架构（BiLSTM-CNN-CRF）的可复现性，属于传统的NLP方法而非现代LLM或Transformer技术。虽然序列标注在搜索中有潜在应用（如命名实体识别），但该研究聚焦于架构复现而非核心推荐/搜索/广告领域的创新，且不涉及LLM、Transformer或异构数据建模等当前关注方向。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 02:49:21
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.10936v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.10936v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.LG</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    We present a reproducibility study of the state-of-the-art neural architecture for sequence labeling proposed by Ma and Hovy (2016)\cite{ma2016end}. The original BiLSTM-CNN-CRF model combines character-level representations via Convolutional Neural Networks (CNNs), word-level context modeling through Bi-directional Long Short-Term Memory networks (BiLSTMs), and structured prediction using Conditional Random Fields (CRFs). This end-to-end approach eliminates the need for hand-crafted features while achieving excellent performance on named entity recognition (NER) and part-of-speech (POS) tagging tasks. Our implementation successfully reproduces the key results, achieving 91.18\% F1-score on CoNLL-2003 NER and demonstrating the model's effectiveness across sequence labeling tasks. We provide a detailed analysis of the architecture components and release an open-source PyTorch implementation to facilitate further research.
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            <a href="https://www.alphaxiv.org/abs/2510.10890v1" target="_blank" rel="noopener noreferrer">
                LLM×MapReduce-V3：通过MCP驱动的分层模块化智能体系统实现交互式深度调研生成
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            LLM$\times$MapReduce-V3: Enabling Interactive In-Depth Survey Generation through a MCP-Driven Hierarchically Modular Agent System
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yu Chao, Siyu Lin, xiaorong wang, Zhu Zhang, Zihan Zhou, Haoyu Wang, Shuo Wang, ...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注基于LLM的调研生成系统，这属于纯粹的LLM应用范畴（AIGC/内容生成）。虽然提到了模块化智能体系统和MapReduce架构，但这些技术并没有明确指向推荐系统、搜索或广告领域的特定应用。论文的核心焦点是交互式调研生成，这与我的关注领域中的直接应用要求不符。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 01:38:37
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.10890v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.10890v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    We introduce LLM x MapReduce-V3, a hierarchically modular agent system designed for long-form survey generation. Building on the prior work, LLM x MapReduce-V2, this version incorporates a multi-agent architecture where individual functional components, such as skeleton initialization, digest construction, and skeleton refinement, are implemented as independent model-context-protocol (MCP) servers. These atomic servers can be aggregated into higher-level servers, creating a hierarchically structured system. A high-level planner agent dynamically orchestrates the workflow by selecting appropriate modules based on their MCP tool descriptions and the execution history. This modular decomposition facilitates human-in-the-loop intervention, affording users greater control and customization over the research process. Through a multi-turn interaction, the system precisely captures the intended research perspectives to generate a comprehensive skeleton, which is then developed into an in-depth survey. Human evaluations demonstrate that our system surpasses representative baselines in both content depth and length, highlighting the strength of MCP-based modular planning.
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            <a href="https://www.alphaxiv.org/abs/2510.10885v1" target="_blank" rel="noopener noreferrer">
                重新思考智能体工作流：在Text2SQL任务中评估基于推理的测试时扩展策略
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            Rethinking Agentic Workflows: Evaluating Inference-Based Test-Time Scaling Strategies in Text2SQL Tasks
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jiajing Guo, Kenil Patel, Jorge Piazentin Ono, Wenbin He, Liu Ren
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注Text2SQL任务中的智能体工作流和测试时扩展策略，这属于特定领域NLP应用。虽然提到了推理和扩展策略，但这些技术主要针对数据库查询生成任务，与推荐系统、搜索或广告的核心技术栈关联度较低。没有明确展示这些方法在RecSys/Search/Ads领域的潜在应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 01:29:54
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.10885v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.10885v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.DB</span></div>
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                    Large language models (LLMs) are increasingly powering Text-to-SQL (Text2SQL) systems, enabling non-expert users to query industrial databases using natural language. While test-time scaling strategies have shown promise in LLM-based solutions, their effectiveness in real-world applications, especially with the latest reasoning models, remains uncertain. In this work, we benchmark six lightweight, industry-oriented test-time scaling strategies and four LLMs, including two reasoning models, evaluating their performance on the BIRD Mini-Dev benchmark. Beyond standard accuracy metrics, we also report inference latency and token consumption, providing insights relevant for practical system deployment. Our findings reveal that Divide-and-Conquer prompting and few-shot demonstrations consistently enhance performance for both general-purpose and reasoning-focused LLMs. However, introducing additional workflow steps yields mixed results, and base model selection plays a critical role. This work sheds light on the practical trade-offs between accuracy, efficiency, and complexity when deploying Text2SQL systems.
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            <a href="https://www.alphaxiv.org/abs/2510.11718v1" target="_blank" rel="noopener noreferrer">
                CodePlot-CoT：通过代码驱动图像进行思维链的数学视觉推理
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            CodePlot-CoT: Mathematical Visual Reasoning by Thinking with Code-Driven Images
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Chengqi Duan, Kaiyue Sun, Rongyao Fang, Manyuan Zhang, Yan Feng, Ying Luo, Yufan...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注数学视觉推理和代码驱动的图像生成，属于视觉语言模型在数学问题解决领域的应用。虽然涉及多模态建模，但其应用场景（数学推理）与推荐系统、搜索或广告的核心技术需求相距甚远，且缺乏明确的跨模态表示学习在商业应用中的潜力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 17:59:55
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11718v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11718v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Recent advances in Large Language Models (LLMs) and Vision Language Models (VLMs) have shown significant progress in mathematical reasoning, yet they still face a critical bottleneck with problems requiring visual assistance, such as drawing auxiliary lines or plotting functions to solve the problems. Most LLMs and VLMs are constrained to text-only reasoning chains, while multimodal unified models that can generate interleaved text and images lack the necessary precision and controllability for such tasks. To address this, we propose CodePlot-CoT, a code-driven Chain-of-Thought paradigm for "thinking with images" in mathematics. Our approach leverages the VLM to generate text reasoning as well as executable plotting code, which is then rendered into images as "visual thought", to solve mathematical problems. To achieve this, we first construct Math-VR, the first large-scale, bilingual dataset and benchmark for Mathematics problems with Visual Reasoning, comprising 178K samples. Second, to create high-quality training data, we develop a state-of-the-art image-to-code converter specialized for parsing complex mathematical figures into codes. Finally, using these training data, we train the CodePlot-CoT model for solving mathematical problems. Experimental results show that our model achieves up to 21% increase over base model on our new benchmark, fully validating the efficacy of our proposed code-driven reasoning paradigm. Our work opens a new direction for multimodal mathematical reasoning and provides the community with the first large-scale dataset, comprehensive benchmark, and strong approach for such problems. To facilitate future research, we make our datasets, code, and pretrained models publicly available at https://github.com/HKU-MMLab/Math-VR-CodePlot-CoT.
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            <a href="https://www.alphaxiv.org/abs/2510.11709v1" target="_blank" rel="noopener noreferrer">
                基于特征叠加中特征间干扰的对抗性攻击
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            Adversarial Attacks Leverage Interference Between Features in Superposition
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Edward Stevinson, Lucas Prieto, Melih Barsbey, Tolga Birdal
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要研究对抗性攻击机制，这属于模型安全/鲁棒性领域，属于明确的无关主题。虽然特征叠加概念在Transformer架构中有所体现，但论文焦点是攻击方法而非核心架构改进或推荐/搜索/广告应用。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 17:59:02
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11709v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11709v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CV</span></div>
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                    Fundamental questions remain about when and why adversarial examples arise in neural networks, with competing views characterising them either as artifacts of the irregularities in the decision landscape or as products of sensitivity to non-robust input features. In this paper, we instead argue that adversarial vulnerability can stem from efficient information encoding in neural networks. Specifically, we show how superposition - where networks represent more features than they have dimensions - creates arrangements of latent representations that adversaries can exploit. We demonstrate that adversarial perturbations leverage interference between superposed features, making attack patterns predictable from feature arrangements. Our framework provides a mechanistic explanation for two known phenomena: adversarial attack transferability between models with similar training regimes and class-specific vulnerability patterns. In synthetic settings with precisely controlled superposition, we establish that superposition suffices to create adversarial vulnerability. We then demonstrate that these findings persist in a ViT trained on CIFAR-10. These findings reveal adversarial vulnerability can be a byproduct of networks' representational compression, rather than flaws in the learning process or non-robust inputs.
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            <a href="https://www.alphaxiv.org/abs/2510.11704v1" target="_blank" rel="noopener noreferrer">
                贝叶斯拓扑卷积神经网络
            </a>
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        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>2/10
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Bayesian Topological Convolutional Neural Nets
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Sarah Harkins Dayton, Hayden Everett, Ioannis Schizas, David L. Boothe Jr., Vasi...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注拓扑数据分析和贝叶斯方法在卷积神经网络中的应用，这属于计算机视觉和理论机器学习的范畴。虽然贝叶斯方法可能对不确定性建模有一定价值，但论文没有明确展示在推荐系统、搜索或广告中的直接应用潜力，且与Transformer架构、LLM技术或异构数据统一建模等当前关注领域关联性较弱。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 17:57:43
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11704v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11704v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Convolutional neural networks (CNNs) have been established as the main workhorse in image data processing; nonetheless, they require large amounts of data to train, often produce overconfident predictions, and frequently lack the ability to quantify the uncertainty of their predictions. To address these concerns, we propose a new Bayesian topological CNN that promotes a novel interplay between topology-aware learning and Bayesian sampling. Specifically, it utilizes information from important manifolds to accelerate training while reducing calibration error by placing prior distributions on network parameters and properly learning appropriate posteriors. One important contribution of our work is the inclusion of a consistency condition in the learning cost, which can effectively modify the prior distributions to improve the performance of our novel network architecture. We evaluate the model on benchmark image classification datasets and demonstrate its superiority over conventional CNNs, Bayesian neural networks (BNNs), and topological CNNs. In particular, we supply evidence that our method provides an advantage in situations where training data is limited or corrupted. Furthermore, we show that the new model allows for better uncertainty quantification than standard BNNs since it can more readily identify examples of out-of-distribution data on which it has not been trained. Our results highlight the potential of our novel hybrid approach for more efficient and robust image classification.
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            <a href="https://www.alphaxiv.org/abs/2510.11687v1" target="_blank" rel="noopener noreferrer">
                超越“模板”：基于单视图的类别无关物体姿态、尺寸与形状估计
            </a>
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Beyond 'Templates': Category-Agnostic Object Pose, Size, and Shape Estimation from a Single View
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jinyu Zhang, Haitao Lin, Jiashu Hou, Xiangyang Xue, Yanwei Fu
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要研究计算机视觉中的3D物体姿态、尺寸和形状估计，属于纯粹的视觉技术领域。虽然标题提到“类别无关”可能暗示泛化能力，但该技术缺乏明确的推荐系统、搜索或广告应用场景，与当前关注的LLM技术、推荐系统核心进展或Transformer架构改进没有直接关联。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 17:49:15
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11687v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11687v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Estimating an object's 6D pose, size, and shape from visual input is a fundamental problem in computer vision, with critical applications in robotic grasping and manipulation. Existing methods either rely on object-specific priors such as CAD models or templates, or suffer from limited generalization across categories due to pose-shape entanglement and multi-stage pipelines. In this work, we propose a unified, category-agnostic framework that simultaneously predicts 6D pose, size, and dense shape from a single RGB-D image, without requiring templates, CAD models, or category labels at test time. Our model fuses dense 2D features from vision foundation models with partial 3D point clouds using a Transformer encoder enhanced by a Mixture-of-Experts, and employs parallel decoders for pose-size estimation and shape reconstruction, achieving real-time inference at 28 FPS. Trained solely on synthetic data from 149 categories in the SOPE dataset, our framework is evaluated on four diverse benchmarks SOPE, ROPE, ObjaversePose, and HANDAL, spanning over 300 categories. It achieves state-of-the-art accuracy on seen categories while demonstrating remarkably strong zero-shot generalization to unseen real-world objects, establishing a new standard for open-set 6D understanding in robotics and embodied AI.
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            <a href="https://www.alphaxiv.org/abs/2510.11650v1" target="_blank" rel="noopener noreferrer">
                InfiniHuman：具有精确控制的无限3D人体生成
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            InfiniHuman: Infinite 3D Human Creation with Precise Control
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yuxuan Xue, Xianghui Xie, Margaret Kostyrko, Gerard Pons-Moll
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于3D人体生成和计算机视觉领域，属于纯粹的视觉内容生成任务。虽然标题提到'精确控制'，但这主要针对3D人体建模的细粒度控制，与推荐系统、搜索或广告中的排序、用户建模等核心问题没有直接关联。该技术可能在某些特定场景下用于广告创意生成，但这属于明确排除的非相关主题。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 17:29:55
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11650v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11650v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Generating realistic and controllable 3D human avatars is a long-standing challenge, particularly when covering broad attribute ranges such as ethnicity, age, clothing styles, and detailed body shapes. Capturing and annotating large-scale human datasets for training generative models is prohibitively expensive and limited in scale and diversity. The central question we address in this paper is: Can existing foundation models be distilled to generate theoretically unbounded, richly annotated 3D human data? We introduce InfiniHuman, a framework that synergistically distills these models to produce richly annotated human data at minimal cost and with theoretically unlimited scalability. We propose InfiniHumanData, a fully automatic pipeline that leverages vision-language and image generation models to create a large-scale multi-modal dataset. User study shows our automatically generated identities are undistinguishable from scan renderings. InfiniHumanData contains 111K identities spanning unprecedented diversity. Each identity is annotated with multi-granularity text descriptions, multi-view RGB images, detailed clothing images, and SMPL body-shape parameters. Building on this dataset, we propose InfiniHumanGen, a diffusion-based generative pipeline conditioned on text, body shape, and clothing assets. InfiniHumanGen enables fast, realistic, and precisely controllable avatar generation. Extensive experiments demonstrate significant improvements over state-of-the-art methods in visual quality, generation speed, and controllability. Our approach enables high-quality avatar generation with fine-grained control at effectively unbounded scale through a practical and affordable solution. We will publicly release the automatic data generation pipeline, the comprehensive InfiniHumanData dataset, and the InfiniHumanGen models at https://yuxuan-xue.com/infini-human.
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            <a href="https://www.alphaxiv.org/abs/2510.11632v1" target="_blank" rel="noopener noreferrer">
                NV3D：基于法向量的三维物体检测，利用空间形状信息
            </a>
        </h3>
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            NV3D: Leveraging Spatial Shape Through Normal Vector-based 3D Object Detection
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Krittin Chaowakarn, Paramin Sangwongngam, Nang Htet Htet Aung, Chalie Charoenlar...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于3D物体检测的计算机视觉技术，通过法向量利用空间形状信息。虽然3D视觉在某些特定搜索场景（如产品搜索）可能有潜在应用，但与推荐系统、搜索或广告的核心技术焦点关联度很低，且未明确涉及Transformer架构或LLM技术。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 17:13:06
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11632v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11632v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.LG</span><span class="category-tag">I.2.6; I.2.9; I.2.10; I.4.8; I.4.10; I.5.1; I.5.4</span></div>
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                    Recent studies in 3D object detection for autonomous vehicles aim to enrich features through the utilization of multi-modal setups or the extraction of local patterns within LiDAR point clouds. However, multi-modal methods face significant challenges in feature alignment, and gaining features locally can be oversimplified for complex 3D object detection tasks. In this paper, we propose a novel model, NV3D, which utilizes local features acquired from voxel neighbors, as normal vectors computed per voxel basis using K-nearest neighbors (KNN) and principal component analysis (PCA). This informative feature enables NV3D to determine the relationship between the surface and pertinent target entities, including cars, pedestrians, or cyclists. During the normal vector extraction process, NV3D offers two distinct sampling strategies: normal vector density-based sampling and FOV-aware bin-based sampling, allowing elimination of up to 55% of data while maintaining performance. In addition, we applied element-wise attention fusion, which accepts voxel features as the query and value and normal vector features as the key, similar to the attention mechanism. Our method is trained on the KITTI dataset and has demonstrated superior performance in car and cyclist detection owing to their spatial shapes. In the validation set, NV3D without sampling achieves 86.60% and 80.18% mean Average Precision (mAP), greater than the baseline Voxel R-CNN by 2.61% and 4.23% mAP, respectively. With both samplings, NV3D achieves 85.54% mAP in car detection, exceeding the baseline by 1.56% mAP, despite roughly 55% of voxels being filtered out.
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            <a href="https://www.alphaxiv.org/abs/2510.11565v1" target="_blank" rel="noopener noreferrer">
                SNAP：面向任意点云中任意目标分割
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            SNAP: Towards Segmenting Anything in Any Point Cloud
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Aniket Gupta, Hanhui Wang, Charles Saunders, Aruni RoyChowdhury, Hanumant Singh,...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于3D点云分割技术，属于计算机视觉领域。虽然点云处理在自动驾驶和机器人领域有应用，但与搜索、推荐、广告系统的核心需求（用户行为建模、内容理解、排序优化）缺乏直接关联。点云分割技术难以转化为处理用户序列、上下文特征或文本内容等推荐系统典型数据模态。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 16:07:00
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11565v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11565v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Interactive 3D point cloud segmentation enables efficient annotation of complex 3D scenes through user-guided prompts. However, current approaches are typically restricted in scope to a single domain (indoor or outdoor), and to a single form of user interaction (either spatial clicks or textual prompts). Moreover, training on multiple datasets often leads to negative transfer, resulting in domain-specific tools that lack generalizability. To address these limitations, we present \textbf{SNAP} (\textbf{S}egment a\textbf{N}ything in \textbf{A}ny \textbf{P}oint cloud), a unified model for interactive 3D segmentation that supports both point-based and text-based prompts across diverse domains. Our approach achieves cross-domain generalizability by training on 7 datasets spanning indoor, outdoor, and aerial environments, while employing domain-adaptive normalization to prevent negative transfer. For text-prompted segmentation, we automatically generate mask proposals without human intervention and match them against CLIP embeddings of textual queries, enabling both panoptic and open-vocabulary segmentation. Extensive experiments demonstrate that SNAP consistently delivers high-quality segmentation results. We achieve state-of-the-art performance on 8 out of 9 zero-shot benchmarks for spatial-prompted segmentation and demonstrate competitive results on all 5 text-prompted benchmarks. These results show that a unified model can match or exceed specialized domain-specific approaches, providing a practical tool for scalable 3D annotation. Project page is at, https://neu-vi.github.io/SNAP/
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            <a href="https://www.alphaxiv.org/abs/2510.11549v1" target="_blank" rel="noopener noreferrer">
                ODI-Bench：多模态大语言模型能否理解沉浸式全向环境？
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            <i class="fa fa-star mr-1"></i>2/10
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            ODI-Bench: Can MLLMs Understand Immersive Omnidirectional Environments?
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Liu Yang, Huiyu Duan, Ran Tao, Juntao Cheng, Sijing Wu, Yunhao Li, Jing Liu, Xio...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文关注多模态大语言模型对全向环境的理解能力，属于纯粹的视觉-语言多模态研究范畴。虽然标题提及'沉浸式环境'，但缺乏与推荐系统、搜索或广告领域的明确关联，且未涉及Transformer架构效率、注意力机制等核心技术进展。全向环境理解在RecSys/Search/Ads中的潜在应用场景非常有限且不明确。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 15:51:47
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11549v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11549v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Omnidirectional images (ODIs) provide full 360x180 view which are widely adopted in VR, AR and embodied intelligence applications. While multi-modal large language models (MLLMs) have demonstrated remarkable performance on conventional 2D image and video understanding benchmarks, their ability to comprehend the immersive environments captured by ODIs remains largely unexplored. To address this gap, we first present ODI-Bench, a novel comprehensive benchmark specifically designed for omnidirectional image understanding. ODI-Bench contains 2,000 high-quality omnidirectional images and over 4,000 manually annotated question-answering (QA) pairs across 10 fine-grained tasks, covering both general-level and spatial-level ODI understanding. Extensive experiments are conducted to benchmark 20 representative MLLMs, including proprietary and open-source models, under both close-ended and open-ended settings. Experimental results reveal that current MLLMs still struggle to capture the immersive context provided by ODIs. To this end, we further introduce Omni-CoT, a training-free method which significantly enhances MLLMs' comprehension ability in the omnidirectional environment through chain-of-thought reasoning across both textual information and visual cues. Both the benchmark and the code will be released upon the publication.
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            <a href="https://www.alphaxiv.org/abs/2510.11538v1" target="_blank" rel="noopener noreferrer">
                大规模激活是扩散变换器中局部细节合成的关键
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        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Massive Activations are the Key to Local Detail Synthesis in Diffusion Transformers
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Chaofan Gan, Zicheng Zhao, Yuanpeng Tu, Xi Chen, Ziran Qin, Tieyuan Chen, Mehrta...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注扩散变换器中的细节合成技术，属于图像生成领域。虽然提到了Transformer架构，但其核心应用是内容生成而非推荐、搜索或广告系统。该技术可能间接影响广告创意生成，但这属于明确排除的非相关主题。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 15:39:13
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11538v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11538v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Diffusion Transformers (DiTs) have recently emerged as a powerful backbone for visual generation. Recent observations reveal \emph{Massive Activations} (MAs) in their internal feature maps, yet their function remains poorly understood. In this work, we systematically investigate these activations to elucidate their role in visual generation. We found that these massive activations occur across all spatial tokens, and their distribution is modulated by the input timestep embeddings. Importantly, our investigations further demonstrate that these massive activations play a key role in local detail synthesis, while having minimal impact on the overall semantic content of output. Building on these insights, we propose \textbf{D}etail \textbf{G}uidance (\textbf{DG}), a MAs-driven, training-free self-guidance strategy to explicitly enhance local detail fidelity for DiTs. Specifically, DG constructs a degraded ``detail-deficient'' model by disrupting MAs and leverages it to guide the original network toward higher-quality detail synthesis. Our DG can seamlessly integrate with Classifier-Free Guidance (CFG), enabling further refinements of fine-grained details. Extensive experiments demonstrate that our DG consistently improves fine-grained detail quality across various pre-trained DiTs (\eg, SD3, SD3.5, and Flux).
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            <a href="https://www.alphaxiv.org/abs/2510.11509v1" target="_blank" rel="noopener noreferrer">
                Situat3DChange：面向多模态大语言模型的场景化三维变化理解数据集
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            Situat3DChange: Situated 3D Change Understanding Dataset for Multimodal Large Language Model
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Ruiping Liu, Junwei Zheng, Yufan Chen, Zirui Wang, Kunyu Peng, Kailun Yang, Jiam...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注3D视觉变化理解数据集和多模态大语言模型，属于纯粹的视觉和多模态研究范畴。虽然涉及多模态大语言模型技术，但缺乏与推荐系统、搜索或广告领域的明确关联，其3D视觉焦点超出了当前关注的技术应用范围。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 15:17:18
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11509v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11509v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Physical environments and circumstances are fundamentally dynamic, yet current 3D datasets and evaluation benchmarks tend to concentrate on either dynamic scenarios or dynamic situations in isolation, resulting in incomplete comprehension. To overcome these constraints, we introduce Situat3DChange, an extensive dataset supporting three situation-aware change understanding tasks following the perception-action model: 121K question-answer pairs, 36K change descriptions for perception tasks, and 17K rearrangement instructions for the action task. To construct this large-scale dataset, Situat3DChange leverages 11K human observations of environmental changes to establish shared mental models and shared situational awareness for human-AI collaboration. These observations, enriched with egocentric and allocentric perspectives as well as categorical and coordinate spatial relations, are integrated using an LLM to support understanding of situated changes. To address the challenge of comparing pairs of point clouds from the same scene with minor changes, we propose SCReasoner, an efficient 3D MLLM approach that enables effective point cloud comparison with minimal parameter overhead and no additional tokens required for the language decoder. Comprehensive evaluation on Situat3DChange tasks highlights both the progress and limitations of MLLMs in dynamic scene and situation understanding. Additional experiments on data scaling and cross-domain transfer demonstrate the task-agnostic effectiveness of using Situat3DChange as a training dataset for MLLMs.
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            <a href="https://www.alphaxiv.org/abs/2510.11417v1" target="_blank" rel="noopener noreferrer">
                基于长期记忆的鲁棒自我-外部对应关系
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Robust Ego-Exo Correspondence with Long-Term Memory
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yijun Hu, Bing Fan, Xin Gu, Haiqing Ren, Dongfang Liu, Heng Fan, Libo Zhang
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文关注计算机视觉中的自我-外部相机对应关系，主要涉及跨视角的时序对齐问题。虽然长期记忆机制在推荐系统中可能有潜在应用，但论文的核心焦点是视觉对应关系而非推荐、搜索或广告领域。该技术可能通过改进用户行为序列建模间接应用于推荐系统，但直接相关性较低。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 13:54:12
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11417v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11417v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Establishing object-level correspondence between egocentric and exocentric views is essential for intelligent assistants to deliver precise and intuitive visual guidance. However, this task faces numerous challenges, including extreme viewpoint variations, occlusions, and the presence of small objects. Existing approaches usually borrow solutions from video object segmentation models, but still suffer from the aforementioned challenges. Recently, the Segment Anything Model 2 (SAM 2) has shown strong generalization capabilities and excellent performance in video object segmentation. Yet, when simply applied to the ego-exo correspondence (EEC) task, SAM 2 encounters severe difficulties due to ineffective ego-exo feature fusion and limited long-term memory capacity, especially for long videos. Addressing these problems, we propose a novel EEC framework based on SAM 2 with long-term memories by presenting a dual-memory architecture and an adaptive feature routing module inspired by Mixture-of-Experts (MoE). Compared to SAM 2, our approach features (i) a Memory-View MoE module which consists of a dual-branch routing mechanism to adaptively assign contribution weights to each expert feature along both channel and spatial dimensions, and (ii) a dual-memory bank system with a simple yet effective compression strategy to retain critical long-term information while eliminating redundancy. In the extensive experiments on the challenging EgoExo4D benchmark, our method, dubbed LM-EEC, achieves new state-of-the-art results and significantly outperforms existing methods and the SAM 2 baseline, showcasing its strong generalization across diverse scenarios. Our code and model are available at https://github.com/juneyeeHu/LM-EEC.
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            <a href="https://www.alphaxiv.org/abs/2510.11346v1" target="_blank" rel="noopener noreferrer">
                不确定性感知ControlNet：通过合成图像生成弥合领域差距
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Uncertainty-Aware ControlNet: Bridging Domain Gaps with Synthetic Image Generation
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Joshua Niemeijer, Jan Ehrhardt, Heinz Handels, Hristina Uzunova
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注图像生成领域的领域适应问题，属于计算机视觉和AIGC范畴。虽然ControlNet技术本身在图像生成中有应用，但论文聚焦于合成图像生成和领域差距，与推荐系统、搜索或广告的核心技术缺乏直接关联，且属于明确的无关主题范围。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 12:41:28
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11346v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11346v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    Generative Models are a valuable tool for the controlled creation of high-quality image data. Controlled diffusion models like the ControlNet have allowed the creation of labeled distributions. Such synthetic datasets can augment the original training distribution when discriminative models, like semantic segmentation, are trained. However, this augmentation effect is limited since ControlNets tend to reproduce the original training distribution. This work introduces a method to utilize data from unlabeled domains to train ControlNets by introducing the concept of uncertainty into the control mechanism. The uncertainty indicates that a given image was not part of the training distribution of a downstream task, e.g., segmentation. Thus, two types of control are engaged in the final network: an uncertainty control from an unlabeled dataset and a semantic control from the labeled dataset. The resulting ControlNet allows us to create annotated data with high uncertainty from the target domain, i.e., synthetic data from the unlabeled distribution with labels. In our scenario, we consider retinal OCTs, where typically high-quality Spectralis images are available with given ground truth segmentations, enabling the training of segmentation networks. The recent development in Home-OCT devices, however, yields retinal OCTs with lower quality and a large domain shift, such that out-of-the-pocket segmentation networks cannot be applied for this type of data. Synthesizing annotated images from the Home-OCT domain using the proposed approach closes this gap and leads to significantly improved segmentation results without adding any further supervision. The advantage of uncertainty-guidance becomes obvious when compared to style transfer: it enables arbitrary domain shifts without any strict learning of an image style. This is also demonstrated in a traffic scene experiment.
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            <a href="https://www.alphaxiv.org/abs/2510.11341v1" target="_blank" rel="noopener noreferrer">
                InternSVG：基于多模态大语言模型的统一可缩放矢量图形任务研究
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            InternSVG: Towards Unified SVG Tasks with Multimodal Large Language Models
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Haomin Wang, Jinhui Yin, Qi Wei, Wenguang Zeng, Lixin Gu, Shenglong Ye, Zhangwei...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于SVG（可缩放矢量图形）任务，属于计算机视觉和图形学领域，与推荐系统、搜索或广告的核心技术没有直接关联。虽然涉及多模态大语言模型，但其应用场景局限于图形处理，缺乏在RecSys/Search/Ads领域的潜在应用价值。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 12:38:04
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11341v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11341v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    General SVG modeling remains challenging due to fragmented datasets, limited transferability of methods across tasks, and the difficulty of handling structural complexity. In response, we leverage the strong transfer and generalization capabilities of multimodal large language models (MLLMs) to achieve unified modeling for SVG understanding, editing, and generation. We present the InternSVG family, an integrated data-benchmark-model suite. At its core is SAgoge, the largest and most comprehensive multimodal dataset for SVG tasks, encompassing both static graphics and dynamic animations. It covers icons, long-sequence illustrations, scientific diagrams, and dynamic animations, supporting tasks of varied difficulty levels and providing deeper hierarchies with richer attributes compared to previous datasets. Based on this resource, we introduce SArena, a companion benchmark with comprehensive task definitions and standardized evaluation that aligns with the domains and difficulty spectrum covered by SAgoge. Building on these foundations, we propose InternSVG, a unified MLLM for SVG understanding, editing, and generation with SVG-specific special tokens, subword-based embedding initialization, and a two-stage training strategy that progresses from short static SVGs to long-sequence illustrations and complex animations. This unified formulation induces positive transfer and improves overall performance. Experiments on SArena and prior benchmark confirm that InternSVG achieves substantial gains and consistently outperforms leading open and proprietary counterparts.
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            <a href="https://www.alphaxiv.org/abs/2510.11302v1" target="_blank" rel="noopener noreferrer">
                监督训练何时物有所值？视觉语言模型时代下目标检测的隐藏经济学
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            When Does Supervised Training Pay Off? The Hidden Economics of Object Detection in the Era of Vision-Language Models
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Samer Al-Hamadani
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注计算机视觉领域的目标检测任务及其训练经济学，属于纯粹的视觉研究范畴。虽然提到了视觉语言模型，但核心焦点是视觉任务的经济分析，没有明确展示在推荐系统、搜索或广告领域的潜在应用价值。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 11:48:48
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11302v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11302v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.LG</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Object detection systems have traditionally relied on supervised learning with manually annotated bounding boxes, achieving high accuracy at the cost of substantial annotation investment. The emergence of Vision-Language Models (VLMs) offers an alternative paradigm enabling zero-shot detection through natural language queries, eliminating annotation requirements but operating with reduced accuracy. This paper presents the first comprehensive cost-effectiveness analysis comparing supervised detection (YOLO) with zero-shot VLM inference (Gemini Flash 2.5). Through systematic evaluation on 1,000 stratified COCO images and 200 diverse product images spanning consumer electronics and rare categories, combined with detailed Total Cost of Ownership modeling, we establish quantitative break-even thresholds governing architecture selection. Our findings reveal that supervised YOLO achieves 91.2% accuracy versus 68.5% for zero-shot Gemini on standard categories, representing a 22.7 percentage point advantage that costs $10,800 in annotation for 100-category systems. However, this advantage justifies investment only beyond 55 million inferences, equivalent to 151,000 images daily for one year. Zero-shot Gemini demonstrates 52.3% accuracy on diverse product categories (ranging from highly web-prevalent consumer electronics at 75-85% to rare specialized equipment at 25-40%) where supervised YOLO achieves 0% due to architectural constraints preventing detection of untrained classes. Cost per Correct Detection analysis reveals substantially lower per-detection costs for Gemini ($0.00050 vs $0.143) at 100,000 inferences despite accuracy deficits. We develop decision frameworks demonstrating that optimal architecture selection depends critically on deployment volume, category stability, budget constraints, and accuracy requirements rather than purely technical performance metrics.
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            <a href="https://www.alphaxiv.org/abs/2510.11295v1" target="_blank" rel="noopener noreferrer">
                视觉问答中的人类不确定性感知数据选择与自动标注
            </a>
        </h3>
        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Human Uncertainty-Aware Data Selection and Automatic Labeling in Visual Question Answering
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jian Lan, Zhicheng Liu, Udo Schlegel, Raoyuan Zhao, Yihong Liu, Hinrich Schütze,...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注视觉问答(VQA)领域的数据选择和标注问题，属于纯粹的视觉-语言多模态研究。虽然标题提到数据选择和自动标注技术，但这些方法主要针对VQA任务设计，没有明确展示在推荐系统、搜索或广告领域的应用潜力。论文的核心焦点是视觉理解而非文本推荐或排序任务。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 11:35:30
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11295v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11295v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Large vision-language models (VLMs) achieve strong performance in Visual Question Answering but still rely heavily on supervised fine-tuning (SFT) with massive labeled datasets, which is costly due to human annotations. Crucially, real-world datasets often exhibit human uncertainty (HU) -- variation in human confidence across annotations -- but standard SFT simply optimizes toward the most frequent label, disregarding HU distributions. This leaves two open questions: How does HU affect SFT, and how can HU be effectively leveraged in training? In this work, we first conduct a systematic evaluation of VLMs across varying HU levels. We have two key findings: (i) surprisingly, high-HU samples contribute little or even degrade model performance, and (ii) naively training on the full dataset yields under-calibrated models that fail to capture HU distributions. Motivated by these findings, we introduce HaDola, a human uncertainty-aware data selection and automatic labeling framework. HaDola operates in four stages -- discriminate, self-annotate, error trigger, and training -- to iteratively identify harmful samples, prioritize informative ones, and bootstrap from a small seed set (5\% of data). Our approach substantially reduces reliance on costly HU annotations and makes VLMs more accurate and better calibrated. Extensive experiments on VQAv2 and VizWiz datasets demonstrate that HaDola consistently matches or outperforms state-of-the-art baselines with less training data. Our work highlights the importance of explicitly modeling HU in SFT, suggesting that better utilization of HU is more effective than merely scaling up dataset size.
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            <a href="https://www.alphaxiv.org/abs/2510.11268v1" target="_blank" rel="noopener noreferrer">
                探索并利用类别向量进行分类器编辑
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            <i class="fa fa-star mr-1"></i>2/10
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            Exploring and Leveraging Class Vectors for Classifier Editing
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jaeik Kim, Jaeyoung Do
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注分类器编辑和类别向量操作，这属于通用的机器学习技术，与推荐系统、搜索或广告的核心进展没有直接关联。虽然分类器编辑技术理论上可以应用于某些推荐系统的模型更新场景，但这种应用过于间接且非核心，不符合当前关注的四大重点领域。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 10:57:51
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11268v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11268v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Image classifiers play a critical role in detecting diseases in medical imaging and identifying anomalies in manufacturing processes. However, their predefined behaviors after extensive training make post hoc model editing difficult, especially when it comes to forgetting specific classes or adapting to distribution shifts. Existing classifier editing methods either focus narrowly on correcting errors or incur extensive retraining costs, creating a bottleneck for flexible editing. Moreover, such editing has seen limited investigation in image classification. To overcome these challenges, we introduce Class Vectors, which capture class-specific representation adjustments during fine-tuning. Whereas task vectors encode task-level changes in weight space, Class Vectors disentangle each class's adaptation in the latent space. We show that Class Vectors capture each class's semantic shift and that classifier editing can be achieved either by steering latent features along these vectors or by mapping them into weight space to update the decision boundaries. We also demonstrate that the inherent linearity and orthogonality of Class Vectors support efficient, flexible, and high-level concept editing via simple class arithmetic. Finally, we validate their utility in applications such as unlearning, environmental adaptation, adversarial defense, and adversarial trigger optimization.
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            <a href="https://www.alphaxiv.org/abs/2510.11223v1" target="_blank" rel="noopener noreferrer">
                基于解耦表情特征探究对话面部动态中的身份信号
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            Investigating Identity Signals in Conversational Facial Dynamics via Disentangled Expression Features
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Masoumeh Chapariniya, Pierre Vuillecard, Jean-Marc Odobez, Volker Dellwo, Teodor...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要研究面部表情动态和身份识别，属于计算机视觉和生物识别领域。虽然涉及对话场景，但其核心技术与推荐系统、搜索或广告的排名优化没有直接关联。面部动态分析可能在人机交互中有应用，但不符合当前关注的LLM、Transformer架构或推荐系统核心技术范畴。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 10:06:25
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11223v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11223v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    This work investigates whether individuals can be identified solely through the pure dynamical components of their facial expressions, independent of static facial appearance. We leverage the FLAME 3D morphable model to achieve explicit disentanglement between facial shape and expression dynamics, extracting frame-by-frame parameters from conversational videos while retaining only expression and jaw coefficients. On the CANDOR dataset of 1,429 speakers in naturalistic conversations, our Conformer model with supervised contrastive learning achieves 61.14\%accuracy on 1,429-way classification -- 458 times above chance -- demonstrating that facial dynamics carry strong identity signatures. We introduce a drift-to-noise ratio (DNR) that quantifies the reliability of shape expression separation by measuring across-session shape changes relative to within-session variability. DNR strongly negatively correlates with recognition performance, confirming that unstable shape estimation compromises dynamic identification. Our findings reveal person-specific signatures in conversational facial dynamics, with implications for social perception and clinical assessment.
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            <a href="https://www.alphaxiv.org/abs/2510.11204v1" target="_blank" rel="noopener noreferrer">
                基于类别原型的对比学习用于多标签和细粒度教育视频分类
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            Class Prototypes based Contrastive Learning for Classifying Multi-Label and Fine-Grained Educational Videos
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Rohit Gupta, Anirban Roy, Claire Christensen, Sujeong Kim, Sarah Gerard, Madelin...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注教育视频分类这一特定领域应用，与推荐系统、搜索或广告的核心技术进展无关。虽然对比学习是一种通用技术，但论文的应用场景过于专业化，缺乏在推荐/搜索/广告领域的直接应用潜力。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 09:36:26
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11204v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11204v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    The recent growth in the consumption of online media by children during early childhood necessitates data-driven tools enabling educators to filter out appropriate educational content for young learners. This paper presents an approach for detecting educational content in online videos. We focus on two widely used educational content classes: literacy and math. For each class, we choose prominent codes (sub-classes) based on the Common Core Standards. For example, literacy codes include `letter names', `letter sounds', and math codes include `counting', `sorting'. We pose this as a fine-grained multilabel classification problem as videos can contain multiple types of educational content and the content classes can get visually similar (e.g., `letter names' vs `letter sounds'). We propose a novel class prototypes based supervised contrastive learning approach that can handle fine-grained samples associated with multiple labels. We learn a class prototype for each class and a loss function is employed to minimize the distances between a class prototype and the samples from the class. Similarly, distances between a class prototype and the samples from other classes are maximized. As the alignment between visual and audio cues are crucial for effective comprehension, we consider a multimodal transformer network to capture the interaction between visual and audio cues in videos while learning the embedding for videos. For evaluation, we present a dataset, APPROVE, employing educational videos from YouTube labeled with fine-grained education classes by education researchers. APPROVE consists of 193 hours of expert-annotated videos with 19 classes. The proposed approach outperforms strong baselines on APPROVE and other benchmarks such as Youtube-8M, and COIN. The dataset is available at https://github.com/rohit-gupta/MMContrast/tree/main/APPROVE
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            <a href="https://www.alphaxiv.org/abs/2510.11178v1" target="_blank" rel="noopener noreferrer">
                BLEnD-Vis：视觉语言模型中多模态文化理解的基准测试
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            BLEnD-Vis: Benchmarking Multimodal Cultural Understanding in Vision Language Models
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Bryan Chen Zhengyu Tan, Zheng Weihua, Zhengyuan Liu, Nancy F. Chen, Hwaran Lee, ...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注视觉语言模型的文化理解基准测试，属于纯粹的VLM评估范畴，与推荐系统、搜索或广告的核心技术进展无关。虽然涉及多模态，但缺乏对异构数据统一建模的直接应用潜力，且评估基准本身属于被排除的纯NLP中心话题。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 09:10:05
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11178v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11178v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.CY</span></div>
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                    As vision-language models (VLMs) are deployed globally, their ability to understand culturally situated knowledge becomes essential. Yet, existing evaluations largely assess static recall or isolated visual grounding, leaving unanswered whether VLMs possess robust and transferable cultural understanding. We introduce BLEnD-Vis, a multimodal, multicultural benchmark designed to evaluate the robustness of everyday cultural knowledge in VLMs across linguistic rephrasings and visual modalities. Building on the BLEnD dataset, BLEnD-Vis constructs 313 culturally grounded question templates spanning 16 regions and generates three aligned multiple-choice formats: (i) a text-only baseline querying from Region $\to$ Entity, (ii) an inverted text-only variant (Entity $\to$ Region), and (iii) a VQA-style version of (ii) with generated images. The resulting benchmark comprises 4,916 images and over 21,000 multiple-choice question (MCQ) instances, validated through human annotation. BLEnD-Vis reveals significant fragility in current VLM cultural knowledge; models exhibit performance drops under linguistic rephrasing and, whilst visual cues often aid performance, low cross-modal consistency highlights challenges in robustly integrating textual and visual understanding, particularly for lower-resource regions. BLEnD-Vis thus provides a crucial testbed for systematically analysing cultural robustness and multimodal grounding, exposing limitations and guiding the development of more culturally competent VLMs.
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            <a href="https://www.alphaxiv.org/abs/2510.11173v1" target="_blank" rel="noopener noreferrer">
                CoPRS：从思维链中学习位置先验用于推理分割
            </a>
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        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>2/10
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            CoPRS: Learning Positional Prior from Chain-of-Thought for Reasoning Segmentation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zhenyu Lu, Liupeng Li, Jinpeng Wang, Yan Feng, Bin Chen, Ke Chen, Yaowei Wang
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注推理分割任务，这是计算机视觉领域的一个特定应用。虽然提到了思维链（Chain-of-Thought）这一LLM相关概念，但其核心应用是视觉分割而非推荐系统、搜索或广告领域。该技术缺乏明确的路径应用于RecSys/Search/Ads的核心问题。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 09:07:54
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11173v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11173v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.MM</span></div>
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                    Existing works on reasoning segmentation either connect hidden features from a language model directly to a mask decoder or represent positions in text, which limits interpretability and semantic detail. To solve this, we present CoPRS, a Multi-modal Chain-of-Thought (MCoT)-based positional perception model that bridges language reasoning to segmentation through a differentiable and interpretable positional prior instantiated as a heatmap. By making the reasoning process clear via MCoT and expressing it as a dense, differentiable heatmap, this interface enhances interpretability and diagnostic analysis and yields more concentrated evidence on the target. A learnable concentration token aggregates features of the image and reasoning text to generate this positional prior, which is decoded to precise masks through a lightweight decoder, providing a direct connection between reasoning and segmentation. Across the RefCOCO series and ReasonSeg, CoPRS matches or surpasses the best reported metrics on each standard split under comparable protocols, with performance at or above prior state of the art across both validation and test partitions. Extensive experiments reveal that the quality of the heatmap strongly influences the resulting mask quality, supporting a consistent association between the reasoning output and downstream mask generation. Collectively, these findings support the utility of this paradigm in bridging reasoning and segmentation and show advantages in concentration driven by reasoning and predicting masks more precisely. Code, checkpoints and logs are released at https://github.com/ZhenyuLU-Heliodore/CoPRS.git.
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            <a href="https://www.alphaxiv.org/abs/2510.11129v1" target="_blank" rel="noopener noreferrer">
                video-SALMONN S：通过记忆机制超越长度限制的流式音频-视觉大语言模型
            </a>
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            video-SALMONN S: Streaming Audio-Visual LLMs Beyond Length Limits via Memory
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Guangzhi Sun, Yixuan Li, Xiaodong Wu, Yudong Yang, Wei Li, Zejun Ma, Chao Zhang
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注音频-视觉模态的流式处理，属于多模态LLM领域，但核心应用场景偏向视频理解和音频处理。虽然提到了长度限制突破和记忆机制，但这些技术主要服务于视频/音频内容理解，与推荐系统、搜索或广告的异构数据处理需求关联较弱，缺乏明确的RecSys/Search/Ads应用潜力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 08:20:15
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11129v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11129v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    Continuous, high-frame-rate, high-resolution processing of long video streams is critical for future AI agents, yet current video-understanding LLMs struggle to scale. Offline, fixed-frame-number methods require the stream length to adapt frame rates; streaming methods constrain memory by merging or discarding tokens, losing information. We propose video-SALMONN S, a streaming audio-visual LLM that, to our knowledge, is the first to process 3-hour videos at 1 FPS and 360p resolution under a fixed memory budget. Our model introduces (i) a test-time-training (TTT) memory module that continually updates token representations to capture long-range dependencies by replacing token merging, and (ii) a prompt-dependent memory reader that selectively retrieves context-relevant content from fixed-size memory. The TTT module is optimised with a Hessian-free conjugate-gradient procedure (TTT_HF) for efficient adaptation. On long-video benchmarks (Video-MME, LVBench, VideoEvalPro), video-SALMONN S sustains high-quality understanding on multi-hour videos with 10k frames and 1M tokens. Our 8B-parameter model achieves 74.2% overall and 67.8% on the Video-MME long split, outperforming both offline and streaming baselines.
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            <a href="https://www.alphaxiv.org/abs/2510.11096v1" target="_blank" rel="noopener noreferrer">
                CoDefend：通过扩散净化与提示优化的跨模态协同防御
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            CoDefend: Cross-Modal Collaborative Defense via Diffusion Purification and Prompt Optimization
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Fengling Zhu, Boshi Liu, Jingyu Hua, Sheng Zhong
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注跨模态防御和安全保护，属于安全领域，与我的核心关注点（推荐系统、搜索、广告中的技术进展）无关。虽然提到了跨模态和提示优化，但这些技术被应用于防御目的，而非提升推荐、搜索或广告系统的性能。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 07:44:54
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11096v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11096v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Multimodal Large Language Models (MLLMs) have achieved remarkable success in tasks such as image captioning, visual question answering, and cross-modal reasoning by integrating visual and textual modalities. However, their multimodal nature also exposes them to adversarial threats, where attackers can perturb either modality or both jointly to induce harmful, misleading, or policy violating outputs. Existing defense strategies, such as adversarial training and input purification, face notable limitations: adversarial training typically improves robustness only against known attacks while incurring high computational costs, whereas conventional purification approaches often suffer from degraded image quality and insufficient generalization to complex multimodal tasks. In this work, we focus on defending the visual modality, which frequently serves as the primary entry point for adversarial manipulation. We propose a supervised diffusion based denoising framework that leverages paired adversarial clean image datasets to fine-tune diffusion models with directional, task specific guidance. Unlike prior unsupervised purification methods such as DiffPure, our approach achieves higher quality reconstructions while significantly improving defense robustness in multimodal tasks. Furthermore, we incorporate prompt optimization as a complementary defense mechanism, enhancing resistance against diverse and unseen attack strategies. Extensive experiments on image captioning and visual question answering demonstrate that our method not only substantially improves robustness but also exhibits strong transferability to unknown adversarial attacks. These results highlight the effectiveness of supervised diffusion based denoising for multimodal defense, paving the way for more reliable and secure deployment of MLLMs in real world applications.
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            <a href="https://www.alphaxiv.org/abs/2510.11027v1" target="_blank" rel="noopener noreferrer">
                Vlaser：具有协同具身推理能力的视觉-语言-动作模型
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            Vlaser: Vision-Language-Action Model with Synergistic Embodied Reasoning
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Ganlin Yang, Tianyi Zhang, Haoran Hao, Weiyun Wang, Yibin Liu, Dehui Wang, Guanz...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注具身智能和机器人控制领域，虽然涉及多模态建模（视觉-语言-动作），但其核心应用场景是物理环境中的机器人交互和动作执行。在搜索、推荐或广告系统中，通常不需要物理动作执行能力，因此与当前关注领域的直接相关性较低。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 05:51:22
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11027v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11027v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    While significant research has focused on developing embodied reasoning capabilities using Vision-Language Models (VLMs) or integrating advanced VLMs into Vision-Language-Action (VLA) models for end-to-end robot control, few studies directly address the critical gap between upstream VLM-based reasoning and downstream VLA policy learning. In this work, we take an initial step toward bridging embodied reasoning with VLA policy learning by introducing Vlaser - a Vision-Language-Action Model with synergistic embodied reasoning capability, which is a foundational vision-language model designed to integrate high-level reasoning with low-level control for embodied agents. Built upon the high-quality Vlaser-6M dataset, Vlaser achieves state-of-the-art performance across a range of embodied reasoning benchmarks - including spatial reasoning, embodied grounding, embodied QA, and task planning. Furthermore, we systematically examine how different VLM initializations affect supervised VLA fine-tuning, offering novel insights into mitigating the domain shift between internet-scale pre-training data and embodied-specific policy learning data. Based on these insights, our approach achieves state-of-the-art results on the WidowX benchmark and competitive performance on the Google Robot benchmark.
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            <a href="https://www.alphaxiv.org/abs/2510.11020v1" target="_blank" rel="noopener noreferrer">
                GeoVLMath：通过跨模态奖励辅助线创建增强视觉语言模型的几何推理能力
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            GeoVLMath: Enhancing Geometry Reasoning in Vision-Language Models via Cross-Modal Reward for Auxiliary Line Creation
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Shasha Guo, Liang Pang, Xi Wang, Yanling Wang, Huawei Shen, Jing Zhang
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注视觉语言模型在几何推理任务上的改进，属于纯粹的视觉-语言交叉领域研究。虽然提到了跨模态奖励机制，但其应用场景仅限于几何数学问题，与推荐系统、搜索或广告领域没有明显的直接关联或潜在应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 05:33:51
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11020v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11020v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    Auxiliary lines are essential for solving complex geometric problems but remain challenging for large vision-language models (LVLMs). Rather than editing diagrams to draw auxiliary lines, which current image editing models struggle to render with geometric precision, we generate textual descriptions of auxiliary-line constructions to better align with the representational strengths of LVLMs. To bridge the gap between textual descriptions and spatial structure, we propose a reinforcement learning framework that enhances diagram-text alignment. At the core of our approach is a cross-modal reward that evaluates how well the generated auxiliary-line description for an original diagram matches a ground-truth auxiliary-line diagram. Built on this reward, we present GeoVLMath, an open-source LVLM tailored to auxiliary-line reasoning in solid geometry. This fine-grained signal drives a GRPO-based RL stage, yielding precise diagram-text alignment. To support training, we develop a scalable data creation pipeline and construct AuxSolidMath, a dataset of 3,018 real-exam geometry problems with paired diagrams and aligned textual fields. At the 3B and 7B scales, GeoVLMath achieves competitive and often superior performance compared with strong open-source and proprietary LVLMs on auxiliary-line reasoning benchmarks.
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            <a href="https://www.alphaxiv.org/abs/2510.11018v1" target="_blank" rel="noopener noreferrer">
                通往鲁棒性的简易路径：基于样本难度的核心集选择
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            The Easy Path to Robustness: Coreset Selection using Sample Hardness
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Pranav Ramesh, Arjun Roy, Deepak Ravikumar, Kaushik Roy, Gopalakrishnan Srinivas...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文关注核心集选择和样本难度评估，这属于数据选择和训练效率的通用机器学习技术。虽然可能间接应用于推荐系统或搜索的数据预处理，但缺乏与LLM、Transformer架构或推荐/搜索/广告领域特定应用的直接联系，且未明确涉及异构数据建模或VLM类比方法。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 05:28:16
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11018v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11018v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.CV</span></div>
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                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Designing adversarially robust models from a data-centric perspective requires understanding which input samples are most crucial for learning resilient features. While coreset selection provides a mechanism for efficient training on data subsets, current algorithms are designed for clean accuracy and fall short in preserving robustness. To address this, we propose a framework linking a sample's adversarial vulnerability to its \textit{hardness}, which we quantify using the average input gradient norm (AIGN) over training. We demonstrate that \textit{easy} samples (with low AIGN) are less vulnerable and occupy regions further from the decision boundary. Leveraging this insight, we present EasyCore, a coreset selection algorithm that retains only the samples with low AIGN for training. We empirically show that models trained on EasyCore-selected data achieve significantly higher adversarial accuracy than those trained with competing coreset methods under both standard and adversarial training. As AIGN is a model-agnostic dataset property, EasyCore is an efficient and widely applicable data-centric method for improving adversarial robustness. We show that EasyCore achieves up to 7\% and 5\% improvement in adversarial accuracy under standard training and TRADES adversarial training, respectively, compared to existing coreset methods.
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            <a href="https://www.alphaxiv.org/abs/2510.11017v1" target="_blank" rel="noopener noreferrer">
                基于视频的人体姿态估计中的全局-局部状态空间模型高分辨率时空建模
            </a>
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        <span class="score-badge bg-gray-100 text-gray-800">
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            High-Resolution Spatiotemporal Modeling with Global-Local State Space Models for Video-Based Human Pose Estimation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Runyang Feng, Hyung Jin Chang, Tze Ho Elden Tse, Boeun Kim, Yi Chang, Yixing Gao
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注计算机视觉领域的人体姿态估计，虽然涉及时空建模技术，但缺乏与推荐系统、搜索或广告的明确联系。状态空间模型在理论上可能用于序列建模，但论文专注于视觉任务，没有展示在推荐/搜索/广告领域的直接应用潜力。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 05:18:27
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11017v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11017v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Modeling high-resolution spatiotemporal representations, including both global dynamic contexts (e.g., holistic human motion tendencies) and local motion details (e.g., high-frequency changes of keypoints), is essential for video-based human pose estimation (VHPE). Current state-of-the-art methods typically unify spatiotemporal learning within a single type of modeling structure (convolution or attention-based blocks), which inherently have difficulties in balancing global and local dynamic modeling and may bias the network to one of them, leading to suboptimal performance. Moreover, existing VHPE models suffer from quadratic complexity when capturing global dependencies, limiting their applicability especially for high-resolution sequences. Recently, the state space models (known as Mamba) have demonstrated significant potential in modeling long-range contexts with linear complexity; however, they are restricted to 1D sequential data. In this paper, we present a novel framework that extends Mamba from two aspects to separately learn global and local high-resolution spatiotemporal representations for VHPE. Specifically, we first propose a Global Spatiotemporal Mamba, which performs 6D selective space-time scan and spatial- and temporal-modulated scan merging to efficiently extract global representations from high-resolution sequences. We further introduce a windowed space-time scan-based Local Refinement Mamba to enhance the high-frequency details of localized keypoint motions. Extensive experiments on four benchmark datasets demonstrate that the proposed model outperforms state-of-the-art VHPE approaches while achieving better computational trade-offs.
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            <a href="https://www.alphaxiv.org/abs/2510.11014v1" target="_blank" rel="noopener noreferrer">
                深入未知：利用生成模型为配置空间规划中的环境不确定性采样先验
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            Into the Unknown: Towards using Generative Models for Sampling Priors of Environment Uncertainty for Planning in Configuration Spaces
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Subhransu S. Bhattacharjee, Hao Lu, Dylan Campbell, Rahul Shome
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注配置空间规划中的生成模型应用，属于机器人学或控制理论领域。虽然涉及生成模型技术，但其在推荐系统、搜索或广告中的潜在应用非常有限且不直接相关。该工作更专注于物理环境建模和规划问题，而非用户行为建模或内容排序等核心领域。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 05:08:48
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11014v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11014v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.RO</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CV</span></div>
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                    Priors are vital for planning under partial observability, yet difficult to obtain in practice. We present a sampling-based pipeline that leverages large-scale pretrained generative models to produce probabilistic priors capturing environmental uncertainty and spatio-semantic relationships in a zero-shot manner. Conditioned on partial observations, the pipeline recovers complete RGB-D point cloud samples with occupancy and target semantics, formulated to be directly useful in configuration-space planning. We establish a Matterport3D benchmark of rooms partially visible through doorways, where a robot must navigate to an unobserved target object. Effective priors for this setting must represent both occupancy and target-location uncertainty in unobserved regions. Experiments show that our approach recovers commonsense spatial semantics consistent with ground truth, yielding diverse, clean 3D point clouds usable in motion planning, highlight the promise of generative models as a rich source of priors for robotic planning.
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            <a href="https://www.alphaxiv.org/abs/2510.11012v1" target="_blank" rel="noopener noreferrer">
                COCO-Tree：用于增强视觉语言模型推理能力的组合式分层概念树
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            COCO-Tree: Compositional Hierarchical Concept Trees for Enhanced Reasoning in Vision Language Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Sanchit Sinha, Guangzhi Xiong, Aidong Zhang
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注视觉语言模型的推理增强技术，属于纯粹的VLM领域研究。虽然提到了概念层次结构和推理能力，但这些技术缺乏明确的推荐系统、搜索或广告应用场景。该工作更偏向于计算机视觉和自然语言处理的交叉领域，而非RecSys/Search/Ads的核心技术需求。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 05:07:13
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11012v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11012v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Compositional reasoning remains a persistent weakness of modern vision language models (VLMs): they often falter when a task hinges on understanding how multiple objects, attributes, and relations interact within an image. Multiple research works have attempted to improve compositionality performance by creative tricks such as improving prompt structure, chain of thought reasoning, etc. A more recent line of work attempts to impart additional reasoning in VLMs using well-trained Large Language Models (LLMs), which are far superior in linguistic understanding than VLMs to compensate for the limited linguistic prowess of VLMs. However, these approaches are either resource-intensive or do not provide an interpretable reasoning process. In this paper, we present 'COCO-Tree' - a novel approach that augments VLM outputs with carefully designed neurosymbolic concept trees learned from LLMs to improve VLM's linguistic reasoning. COCO-Tree's beam search-inspired reasoning process boosts compositionality performance and provides a rationale behind VLM predictions. Empirical results on four compositionality benchmarks, Winoground, EqBench, ColorSwap, and SugarCrepe, in seven different open-source VLMs with varying sizes, demonstrate that COCO-Tree significantly improves compositional generalization by 5-10% over baselines.
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            <a href="https://www.alphaxiv.org/abs/2510.11000v1" target="_blank" rel="noopener noreferrer">
                ContextGen：基于上下文布局锚定的身份一致性多实例生成
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            ContextGen: Contextual Layout Anchoring for Identity-Consistent Multi-Instance Generation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Ruihang Xu, Dewei Zhou, Fan Ma, Yi Yang
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注多实例生成中的身份一致性布局锚定，属于AIGC和内容生成领域。虽然提到了上下文概念，但这与推荐系统、搜索或广告中的上下文特征建模有本质区别，没有明确的RecSys/Search/Ads应用潜力。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 04:21:19
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11000v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11000v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Multi-instance image generation (MIG) remains a significant challenge for modern diffusion models due to key limitations in achieving precise control over object layout and preserving the identity of multiple distinct subjects. To address these limitations, we introduce ContextGen, a novel Diffusion Transformer framework for multi-instance generation that is guided by both layout and reference images. Our approach integrates two key technical contributions: a Contextual Layout Anchoring (CLA) mechanism that incorporates the composite layout image into the generation context to robustly anchor the objects in their desired positions, and Identity Consistency Attention (ICA), an innovative attention mechanism that leverages contextual reference images to ensure the identity consistency of multiple instances. Recognizing the lack of large-scale, hierarchically-structured datasets for this task, we introduce IMIG-100K, the first dataset with detailed layout and identity annotations. Extensive experiments demonstrate that ContextGen sets a new state-of-the-art, outperforming existing methods in control precision, identity fidelity, and overall visual quality.
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            <a href="https://www.alphaxiv.org/abs/2510.10980v1" target="_blank" rel="noopener noreferrer">
                关于Barlow Twins最优表示效率的研究：一种信息几何解释
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            On the Optimal Representation Efficiency of Barlow Twins: An Information-Geometric Interpretation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Di Zhang
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要研究自监督学习中的Barlow Twins方法及其表示效率的理论分析，属于基础表示学习范畴。虽然表示学习是推荐和搜索系统的底层技术，但论文聚焦于信息几何解释和理论效率分析，缺乏明确的RecSys/Search/Ads应用场景，且不涉及Transformer架构或LLM技术的直接进展。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 03:41:27
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.10980v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.10980v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.CV</span><span class="category-tag">cs.IT</span><span class="category-tag">math.IT</span><span class="category-tag">math.ST</span><span class="category-tag">stat.ML</span><span class="category-tag">stat.TH</span><span class="category-tag">68T07</span><span class="category-tag">62B11</span><span class="category-tag">94A17</span><span class="category-tag">53B12</span><span class="category-tag">I.2.6; I.5.1; G.3; H.1.1</span></div>
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                    Self-supervised learning (SSL) has achieved remarkable success by learning meaningful representations without labeled data. However, a unified theoretical framework for understanding and comparing the efficiency of different SSL paradigms remains elusive. In this paper, we introduce a novel information-geometric framework to quantify representation efficiency. We define representation efficiency $\eta$ as the ratio between the effective intrinsic dimension of the learned representation space and its ambient dimension, where the effective dimension is derived from the spectral properties of the Fisher Information Matrix (FIM) on the statistical manifold induced by the encoder. Within this framework, we present a theoretical analysis of the Barlow Twins method. Under specific but natural assumptions, we prove that Barlow Twins achieves optimal representation efficiency ($\eta = 1$) by driving the cross-correlation matrix of representations towards the identity matrix, which in turn induces an isotropic FIM. This work provides a rigorous theoretical foundation for understanding the effectiveness of Barlow Twins and offers a new geometric perspective for analyzing SSL algorithms.
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            <a href="https://www.alphaxiv.org/abs/2510.10973v1" target="_blank" rel="noopener noreferrer">
                Chart-RVR：基于可验证奖励的强化学习用于可解释图表推理
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            Chart-RVR: Reinforcement Learning with Verifiable Rewards for Explainable Chart Reasoning
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Sanchit Sinha, Oana Frunza, Kashif Rasul, Yuriy Nevmyvaka, Aidong Zhang
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注图表推理领域的强化学习应用，属于特定领域的推理任务。虽然涉及强化学习技术，但缺乏与推荐系统、搜索或广告领域的明确关联。图表推理的应用场景主要局限于数据可视化和文档理解，与我的核心关注领域重叠度较低。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 03:25:35
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.10973v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.10973v1
                </a>
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.LG</span></div>
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                    The capabilities of Large Vision-Language Models (LVLMs) have reached state-of-the-art on many visual reasoning tasks, including chart reasoning, yet they still falter on out-of-distribution (OOD) data, and degrade further when asked to produce their chain-of-thought (CoT) rationales, limiting explainability. We present Chart-RVR, a general framework that fine-tunes LVLMs to be more robust and explainable for chart reasoning by coupling Group Relative Policy Optimization (GRPO) with automatically verifiable rewards. Our framework comprises of three rewards that maximize: (i) correct chart-type classification, (ii) faithful chart table reconstruction, and (iii) process conformity. Applied to 3-billion-parameter LVLMs, Chart-RVR consistently outperforms standard supervised fine-tuning (SFT) on both in-distribution and out-of-distribution datasets, closing the OOD performance gap while improving rationale fidelity. The resulting models, the Chart-RVR-3B series, achieve state-of-the-art results on six chart-reasoning benchmarks spanning in-domain and OOD settings, surpassing all existing models of comparable size. Beyond accuracy, Chart-RVR yields more interpretable CoT rationales, strengthening trust and reliability - showcasing the power of verifiable rewards with GRPO for training reliable, interpretable chart-reasoning models.
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            <a href="https://www.alphaxiv.org/abs/2510.10969v1" target="_blank" rel="noopener noreferrer">
                IUT-Plug：一种用于交错图像-文本生成的插件工具
            </a>
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            IUT-Plug: A Plug-in tool for Interleaved Image-Text Generation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zeteng Lin, Xingxing Li, Wen You, Xiaoyang Li, Zehan Lu, Yujun Cai, Jing Tang
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于图像-文本交错生成技术，属于AIGC和内容生成领域。虽然标题提到插件工具，但核心是跨模态生成任务，与推荐系统、搜索或广告的排序和匹配问题没有直接关联，属于被排除的纯LLM中心化主题。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 03:19:45
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                <a href="https://arxiv.org/abs/2510.10969v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.10969v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Existing vision language models (VLMs), including GPT-4 and DALL-E, often struggle to preserve logic, object identity, and style in multimodal image-text generation. This limitation significantly hinders the generalization capability of VLMs in complex image-text input-output scenarios. To address this issue, we propose IUT-Plug, a module grounded in an Image Understanding Tree (IUT), which enhances existing interleaved VLMs through explicit structured reasoning, thereby mitigating context drift in logic, entity identity, and style. The proposed framework operates in two stages. (1) A dynamic IUT-Plug extraction module parses visual scenes into hierarchical symbolic structures. (2) A coordinated narrative-flow and image synthesis mechanism ensures cross-modal consistency. To evaluate our approach, we construct a novel benchmark based on 3,000 real human-generated question-answer pairs over fine-tuned large models, introducing a dynamic evaluation protocol for quantifying context drift in interleaved VLMs. Experimental results demonstrate that IUT-Plug not only improves accuracy on established benchmarks but also effectively alleviates the three critical forms of context drift across diverse multimodal question answering (QA) scenarios.
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            <a href="https://www.alphaxiv.org/abs/2510.10954v1" target="_blank" rel="noopener noreferrer">
                未见建筑环境中可泛化人类空间偏好预测的神经网络架构比较评估
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            Comparative Evaluation of Neural Network Architectures for Generalizable Human Spatial Preference Prediction in Unseen Built Environments
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Maral Doctorarastoo, Katherine A. Flanigan, Mario Bergés, Christopher McComb
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于建筑环境中的空间偏好预测，这属于特定领域应用而非核心推荐系统、搜索或广告领域。虽然涉及神经网络架构比较，但其应用场景（建筑环境）与我的关注点（RecSys/Search/Ads）相关性很低，且未明确涉及Transformer架构或LLM技术在这些领域的潜在应用。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 03:04:48
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.10954v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.10954v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CE</span><span class="category-tag">cs.CV</span><span class="category-tag">cs.LG</span><span class="category-tag">cs.MA</span></div>
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                    The capacity to predict human spatial preferences within built environments is instrumental for developing Cyber-Physical-Social Infrastructure Systems (CPSIS). A significant challenge in this domain is the generalizability of preference models, particularly their efficacy in predicting preferences within environmental configurations not encountered during training. While deep learning models have shown promise in learning complex spatial and contextual dependencies, it remains unclear which neural network architectures are most effective at generalizing to unseen layouts. To address this, we conduct a comparative study of Graph Neural Networks, Convolutional Neural Networks, and standard feedforward Neural Networks using synthetic data generated from a simplified and synthetic pocket park environment. Beginning with this illustrative case study, allows for controlled analysis of each model's ability to transfer learned preference patterns to unseen spatial scenarios. The models are evaluated based on their capacity to predict preferences influenced by heterogeneous physical, environmental, and social features. Generalizability score is calculated using the area under the precision-recall curve for the seen and unseen layouts. This generalizability score is appropriate for imbalanced data, providing insights into the suitability of each neural network architecture for preference-aware human behavior modeling in unseen built environments.
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            <a href="https://www.alphaxiv.org/abs/2510.10947v1" target="_blank" rel="noopener noreferrer">
                面向生成先验逆问题的分布偏移不确定性估计
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            Towards Distribution-Shift Uncertainty Estimation for Inverse Problems with Generative Priors
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Namhoon Kim, Sara Fridovich-Keil
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注逆问题中的不确定性估计和分布偏移，这属于通用的机器学习方法研究。虽然生成先验可能与生成模型相关，但论文的核心焦点是逆问题和不确定性量化，在推荐系统、搜索或广告中的直接应用潜力有限。没有明确证据表明该技术专门针对推荐、搜索或广告领域的特定挑战。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 02:58:26
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.10947v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.10947v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Generative models have shown strong potential as data-driven priors for solving inverse problems such as reconstructing medical images from undersampled measurements. While these priors improve reconstruction quality with fewer measurements, they risk hallucinating features when test images lie outside the training distribution. Existing uncertainty quantification methods in this setting (i) require an in-distribution calibration dataset, which may not be available, (ii) provide heuristic rather than statistical estimates, or (iii) quantify uncertainty from model capacity or limited measurements rather than distribution shift. We propose an instance-level, calibration-free uncertainty indicator that is sensitive to distribution shift, requires no knowledge of the training distribution, and incurs no retraining cost. Our key hypothesis is that reconstructions of in-distribution images remain stable under random measurement variations, while reconstructions of out-of-distribution (OOD) images exhibit greater instability. We use this stability as a proxy for detecting distribution shift. Our proposed OOD indicator is efficiently computable for any computational imaging inverse problem; we demonstrate it on tomographic reconstruction of MNIST digits, where a learned proximal network trained only on digit "0" is evaluated on all ten digits. Reconstructions of OOD digits show higher variability and correspondingly higher reconstruction error, validating this indicator. These results suggest a deployment strategy that pairs generative priors with lightweight guardrails, enabling aggressive measurement reduction for in-distribution cases while automatically warning when priors are applied out of distribution.
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            <a href="https://www.alphaxiv.org/abs/2510.11003v1" target="_blank" rel="noopener noreferrer">
                基于FBS模型的制造系统故障原因推断维护记录累积方法
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        <div class="mb-2 text-base text-gray-700">
            FBS Model-based Maintenance Record Accumulation for Failure-Cause Inference in Manufacturing Systems
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Takuma Fujiu, Sho Okazaki, Kohei Kaminishi, Yuji Nakata, Shota Hamamoto, Kenshin...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于制造系统中的故障诊断和维护记录管理，属于工业工程和制造领域的具体应用。这与推荐系统、搜索或广告的核心技术领域完全无关，也不涉及LLM、Transformer架构或异构数据建模等任何相关技术。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 04:37:40
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11003v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11003v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.AI</span><span class="category-tag">cs.IR</span></div>
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                    In manufacturing systems, identifying the causes of failures is crucial for maintaining and improving production efficiency. In knowledge-based failure-cause inference, it is important that the knowledge base (1) explicitly structures knowledge about the target system and about failures, and (2) contains sufficiently long causal chains of failures. In this study, we constructed Diagnostic Knowledge Ontology and proposed a Function-Behavior-Structure (FBS) model-based maintenance-record accumulation method based on it. Failure-cause inference using the maintenance records accumulated by the proposed method showed better agreement with the set of candidate causes enumerated by experts, especially in difficult cases where the number of related cases is small and the vocabulary used differs. In the future, it will be necessary to develop inference methods tailored to these maintenance records, build a user interface, and carry out validation on larger and more diverse systems. Additionally, this approach leverages the understanding and knowledge of the target in the design phase to support knowledge accumulation and problem solving during the maintenance phase, and it is expected to become a foundation for knowledge sharing across the entire engineering chain in the future.
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            <a href="https://www.alphaxiv.org/abs/2510.11586v1" target="_blank" rel="noopener noreferrer">
                调查问卷响应生成：使用大型语言模型在计算机模拟中生成封闭式调查问卷响应
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            Survey Response Generation: Generating Closed-Ended Survey Responses In-Silico with Large Language Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Georg Ahnert, Anna-Carolina Haensch, Barbara Plank, Markus Strohmaier
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于使用LLM生成封闭式调查问卷响应，这属于纯粹的LLM应用场景，与推荐系统、搜索或广告的核心技术无关。虽然涉及LLM技术，但应用场景是调查问卷生成，而非RecSys/Search/Ads领域的任何相关任务，因此相关性极低。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 16:29:19
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11586v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11586v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.CY</span></div>
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                    Many in-silico simulations of human survey responses with large language models (LLMs) focus on generating closed-ended survey responses, whereas LLMs are typically trained to generate open-ended text instead. Previous research has used a diverse range of methods for generating closed-ended survey responses with LLMs, and a standard practice remains to be identified. In this paper, we systematically investigate the impact that various Survey Response Generation Methods have on predicted survey responses. We present the results of 32 mio. simulated survey responses across 8 Survey Response Generation Methods, 4 political attitude surveys, and 10 open-weight language models. We find significant differences between the Survey Response Generation Methods in both individual-level and subpopulation-level alignment. Our results show that Restricted Generation Methods perform best overall, and that reasoning output does not consistently improve alignment. Our work underlines the significant impact that Survey Response Generation Methods have on simulated survey responses, and we develop practical recommendations on the application of Survey Response Generation Methods.
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                规避基于推理的安全护栏的实用技巧集
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            Bag of Tricks for Subverting Reasoning-based Safety Guardrails
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Shuo Chen, Zhen Han, Haokun Chen, Bailan He, Shengyun Si, Jingpei Wu, Philip Tor...
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于对抗性攻击和安全规避技术，这属于安全领域的范畴，与我的关注点无关。论文内容涉及如何绕过LLM的安全机制，这与推荐系统、搜索或广告中的核心技术进步、Transformer架构改进或LLM应用完全无关。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 16:16:44
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11570v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11570v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CR</span><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Recent reasoning-based safety guardrails for Large Reasoning Models (LRMs), such as deliberative alignment, have shown strong defense against jailbreak attacks. By leveraging LRMs' reasoning ability, these guardrails help the models to assess the safety of user inputs before generating final responses. The powerful reasoning ability can analyze the intention of the input query and will refuse to assist once it detects the harmful intent hidden by the jailbreak methods. Such guardrails have shown a significant boost in defense, such as the near-perfect refusal rates on the open-source gpt-oss series. Unfortunately, we find that these powerful reasoning-based guardrails can be extremely vulnerable to subtle manipulation of the input prompts, and once hijacked, can lead to even more harmful results. Specifically, we first uncover a surprisingly fragile aspect of these guardrails: simply adding a few template tokens to the input prompt can successfully bypass the seemingly powerful guardrails and lead to explicit and harmful responses. To explore further, we introduce a bag of jailbreak methods that subvert the reasoning-based guardrails. Our attacks span white-, gray-, and black-box settings and range from effortless template manipulations to fully automated optimization. Along with the potential for scalable implementation, these methods also achieve alarmingly high attack success rates (e.g., exceeding 90% across 5 different benchmarks on gpt-oss series on both local host models and online API services). Evaluations across various leading open-source LRMs confirm that these vulnerabilities are systemic, underscoring the urgent need for stronger alignment techniques for open-sourced LRMs to prevent malicious misuse. Code is open-sourced at https://chenxshuo.github.io/bag-of-tricks.
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            <a href="https://www.alphaxiv.org/abs/2510.11277v1" target="_blank" rel="noopener noreferrer">
                面向证据稀缺场景下的实时虚假新闻检测
            </a>
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            Towards Real-Time Fake News Detection under Evidence Scarcity
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Guangyu Wei, Ke Han, Yueming Lyu, Yu Luo, Yue Jiang, Caifeng Shan, Nicu Sebe
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于虚假新闻检测这一特定领域，属于内容安全和可信度验证范畴，与推荐系统、搜索或广告的核心技术进展没有直接关联。虚假新闻检测主要涉及内容理解、可信度评估等NLP应用，而非推荐排序、用户建模或广告投放等核心业务场景。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 11:11:46
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11277v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11277v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Fake news detection becomes particularly challenging in real-time scenarios, where emerging events often lack sufficient supporting evidence. Existing approaches often rely heavily on external evidence and therefore struggle to generalize under evidence scarcity. To address this issue, we propose Evaluation-Aware Selection of Experts (EASE), a novel framework for real-time fake news detection that dynamically adapts its decision-making process according to the assessed sufficiency of available evidence. EASE introduces a sequential evaluation mechanism comprising three independent perspectives: (1) Evidence-based evaluation, which assesses evidence and incorporates it into decision-making only when the evidence is sufficiently supportive; (2) Reasoning-based evaluation, which leverages the world knowledge of large language models (LLMs) and applies them only when their reliability is adequately established; and (3) Sentiment-based fallback, which integrates sentiment cues when neither evidence nor reasoning is reliable. To enhance the accuracy of evaluation processes, EASE employs instruction tuning with pseudo labels to guide each evaluator in justifying its perspective-specific knowledge through interpretable reasoning. Furthermore, the expert modules integrate the evaluators' justified assessments with the news content to enable evaluation-aware decision-making, thereby enhancing overall detection accuracy. Moreover, we introduce RealTimeNews-25, a new benchmark comprising recent news for evaluating model generalization on emerging news with limited evidence. Extensive experiments demonstrate that EASE not only achieves state-of-the-art performance across multiple benchmarks, but also significantly improves generalization to real-time news. The code and dataset are available: https://github.com/wgyhhhh/EASE.
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            <a href="https://www.alphaxiv.org/abs/2510.11254v1" target="_blank" rel="noopener noreferrer">
                心理测量测试对大型语言模型有效吗？关于性别歧视、种族歧视和道德观的测试评估
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        <div class="mb-2 text-base text-gray-700">
            Do Psychometric Tests Work for Large Language Models? Evaluation of Tests on Sexism, Racism, and Morality
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jana Jung, Marlene Lutz, Indira Sen, Markus Strohmaier
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于LLM的伦理评估、偏见检测和心理测量测试，这些都属于被明确排除的伦理、公平性和非技术性话题范畴。论文内容不涉及推荐系统、搜索或广告的技术进步，也没有展示任何在推荐系统、搜索或广告领域的潜在应用价值。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 10:43:49
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                <a href="https://arxiv.org/abs/2510.11254v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11254v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    Psychometric tests are increasingly used to assess psychological constructs in large language models (LLMs). However, it remains unclear whether these tests -- originally developed for humans -- yield meaningful results when applied to LLMs. In this study, we systematically evaluate the reliability and validity of human psychometric tests for three constructs: sexism, racism, and morality. We find moderate reliability across multiple item and prompt variations. Validity is evaluated through both convergent (i.e., testing theory-based inter-test correlations) and ecological approaches (i.e., testing the alignment between tests scores and behavior in real-world downstream tasks). Crucially, we find that psychometric test scores do not align, and in some cases even negatively correlate with, model behavior in downstream tasks, indicating low ecological validity. Our results highlight that systematic evaluations of psychometric tests is essential before interpreting their scores. They also suggest that psychometric tests designed for humans cannot be applied directly to LLMs without adaptation.
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            <a href="https://www.alphaxiv.org/abs/2510.11238v1" target="_blank" rel="noopener noreferrer">
                基于内容的攻击：自动化事实核查是一个AI安全问题
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            Attacks by Content: Automated Fact-checking is an AI Security Issue
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Michael Schlichtkrull
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文关注AI安全和自动化事实核查，这属于安全领域，明确在无关主题列表中。论文标题没有表明与推荐系统、搜索或广告的核心进展、使能技术或直接应用有任何关联。安全相关主题被明确排除在关注范围之外。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 10:18:48
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11238v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11238v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    When AI agents retrieve and reason over external documents, adversaries can manipulate the data they receive to subvert their behaviour. Previous research has studied indirect prompt injection, where the attacker injects malicious instructions. We argue that injection of instructions is not necessary to manipulate agents - attackers could instead supply biased, misleading, or false information. We term this an attack by content. Existing defenses, which focus on detecting hidden commands, are ineffective against attacks by content. To defend themselves and their users, agents must critically evaluate retrieved information, corroborating claims with external evidence and evaluating source trustworthiness. We argue that this is analogous to an existing NLP task, automated fact-checking, which we propose to repurpose as a cognitive self-defense tool for agents.
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            <a href="https://www.alphaxiv.org/abs/2510.11233v1" target="_blank" rel="noopener noreferrer">
                CNSocialDepress：一个用于抑郁症风险检测与结构化分析的中文社交媒体数据集
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            CNSocialDepress: A Chinese Social Media Dataset for Depression Risk Detection and Structured Analysis
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jinyuan Xu, Tian Lan, Xintao Yu, Xue He, Hezhi Zhang, Ying Wang, Pierre Magistry...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文聚焦于医疗领域的抑郁症检测数据集构建，属于明确的医学应用范畴。虽然涉及社交媒体数据，但其核心目标是心理健康诊断而非推荐系统、搜索或广告的技术进步，完全落在用户指定的不相关主题范围内。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 10:14:18
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11233v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11233v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    Depression is a pressing global public health issue, yet publicly available Chinese-language resources for risk detection remain scarce and are mostly limited to binary classification. To address this limitation, we release CNSocialDepress, a benchmark dataset for depression risk detection from Chinese social media posts. The dataset contains 44,178 texts from 233 users, within which psychological experts annotated 10,306 depression-related segments. CNSocialDepress provides binary risk labels together with structured multi-dimensional psychological attributes, enabling interpretable and fine-grained analysis of depressive signals. Experimental results demonstrate its utility across a wide range of NLP tasks, including structured psychological profiling and fine-tuning of large language models for depression detection. Comprehensive evaluations highlight the dataset's effectiveness and practical value for depression risk identification and psychological analysis, thereby providing insights to mental health applications tailored for Chinese-speaking populations.
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            <a href="https://www.alphaxiv.org/abs/2510.11222v1" target="_blank" rel="noopener noreferrer">
                基于Transformer模型的多领域道德情感分类中公平性指标设计探索
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            Fairness Metric Design Exploration in Multi-Domain Moral Sentiment Classification using Transformer-Based Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Battemuulen Naranbat, Seyed Sahand Mohammadi Ziabari, Yousuf Nasser Al Husaini, ...
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文明确聚焦于公平性指标设计，这属于明确的无关主题。虽然涉及Transformer模型，但核心关注点是公平性评估而非技术改进或应用，与搜索、推荐或广告系统的技术进展无关。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 10:05:57
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11222v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11222v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                    Ensuring fairness in natural language processing for moral sentiment classification is challenging, particularly under cross-domain shifts where transformer models are increasingly deployed. Using the Moral Foundations Twitter Corpus (MFTC) and Moral Foundations Reddit Corpus (MFRC), this work evaluates BERT and DistilBERT in a multi-label setting with in-domain and cross-domain protocols. Aggregate performance can mask disparities: we observe pronounced asymmetry in transfer, with Twitter->Reddit degrading micro-F1 by 14.9% versus only 1.5% for Reddit->Twitter. Per-label analysis reveals fairness violations hidden by overall scores; notably, the authority label exhibits Demographic Parity Differences of 0.22-0.23 and Equalized Odds Differences of 0.40-0.41. To address this gap, we introduce the Moral Fairness Consistency (MFC) metric, which quantifies the cross-domain stability of moral foundation detection. MFC shows strong empirical validity, achieving a perfect negative correlation with Demographic Parity Difference (rho = -1.000, p < 0.001) while remaining independent of standard performance metrics. Across labels, loyalty demonstrates the highest consistency (MFC = 0.96) and authority the lowest (MFC = 0.78). These findings establish MFC as a complementary, diagnosis-oriented metric for fairness-aware evaluation of moral reasoning models, enabling more reliable deployment across heterogeneous linguistic contexts. .
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                使用多模态扰动评估医学视觉语言模型的推理忠实性
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            Evaluating Reasoning Faithfulness in Medical Vision-Language Models using Multimodal Perturbations
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Johannes Moll, Markus Graf, Tristan Lemke, Nicolas Lenhart, Daniel Truhn, Jean-B...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学领域的视觉语言模型评估和忠实性分析，属于医学应用和评估基准范畴。这些主题明确属于不相关领域，与推荐系统、搜索或广告的核心技术进展没有直接关联。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 09:28:22
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11196v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11196v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.CV</span></div>
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                    Vision-language models (VLMs) often produce chain-of-thought (CoT) explanations that sound plausible yet fail to reflect the underlying decision process, undermining trust in high-stakes clinical use. Existing evaluations rarely catch this misalignment, prioritizing answer accuracy or adherence to formats. We present a clinically grounded framework for chest X-ray visual question answering (VQA) that probes CoT faithfulness via controlled text and image modifications across three axes: clinical fidelity, causal attribution, and confidence calibration. In a reader study (n=4), evaluator-radiologist correlations fall within the observed inter-radiologist range for all axes, with strong alignment for attribution (Kendall's $\tau_b=0.670$), moderate alignment for fidelity ($\tau_b=0.387$), and weak alignment for confidence tone ($\tau_b=0.091$), which we report with caution. Benchmarking six VLMs shows that answer accuracy and explanation quality are decoupled, acknowledging injected cues does not ensure grounding, and text cues shift explanations more than visual cues. While some open-source models match final answer accuracy, proprietary models score higher on attribution (25.0% vs. 1.4%) and often on fidelity (36.1% vs. 31.7%), highlighting deployment risks and the need to evaluate beyond final answer accuracy.
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            <a href="https://www.alphaxiv.org/abs/2510.11167v1" target="_blank" rel="noopener noreferrer">
                弥合仇恨言论检测中的差距：面向低资源伊比利亚语言的元集合与基准
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            Bridging Gaps in Hate Speech Detection: Meta-Collections and Benchmarks for Low-Resource Iberian Languages
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Paloma Piot, José Ramom Pichel Campos, Javier Parapar
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于仇恨言论检测这一特定NLP任务，属于内容安全领域，与推荐系统、搜索或广告的核心技术无关。虽然涉及多语言处理，但主要关注低资源语言的基准构建，没有明显的RecSys/Search/Ads应用潜力。这属于纯粹的NLP应用研究，不在当前关注范围内。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 08:58:02
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11167v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11167v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    Hate speech poses a serious threat to social cohesion and individual well-being, particularly on social media, where it spreads rapidly. While research on hate speech detection has progressed, it remains largely focused on English, resulting in limited resources and benchmarks for low-resource languages. Moreover, many of these languages have multiple linguistic varieties, a factor often overlooked in current approaches. At the same time, large language models require substantial amounts of data to perform reliably, a requirement that low-resource languages often cannot meet. In this work, we address these gaps by compiling a meta-collection of hate speech datasets for European Spanish, standardised with unified labels and metadata. This collection is based on a systematic analysis and integration of existing resources, aiming to bridge the data gap and support more consistent and scalable hate speech detection. We extended this collection by translating it into European Portuguese and into a Galician standard that is more convergent with Spanish and another Galician variant that is more convergent with Portuguese, creating aligned multilingual corpora. Using these resources, we establish new benchmarks for hate speech detection in Iberian languages. We evaluate state-of-the-art large language models in zero-shot, few-shot, and fine-tuning settings, providing baseline results for future research. Moreover, we perform a cross-lingual analysis with our target languages. Our findings underscore the importance of multilingual and variety-aware approaches in hate speech detection and offer a foundation for improved benchmarking in underrepresented European languages.
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            <a href="https://www.alphaxiv.org/abs/2510.11098v1" target="_blank" rel="noopener noreferrer">
                VCB基准：面向音频基础大语言模型对话代理的评估基准
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            VCB Bench: An Evaluation Benchmark for Audio-Grounded Large Language Model Conversational Agents
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jiliang Hu, Wenfu Wang, Zuchao Li, Chenxing Li, Yiyang Zhao, Hanzhao Li, Liqiang...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于音频模态的LLM对话代理评估基准，属于纯粹的评估基准研究。虽然涉及LLM技术，但专注于音频对话评估这一特定领域，与推荐系统、搜索或广告的核心技术进展缺乏直接关联，且评估基准本身属于被排除的无关主题。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 07:45:52
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11098v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11098v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.SD</span><span class="category-tag">cs.CL</span></div>
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                    Recent advances in large audio language models (LALMs) have greatly enhanced multimodal conversational systems. However, existing benchmarks remain limited -- they are mainly English-centric, rely on synthetic speech, and lack comprehensive, discriminative evaluation across multiple dimensions. To address these gaps, we present Voice Chat Bot Bench (VCB Bench) -- a high-quality Chinese benchmark built entirely on real human speech. VCB Bench evaluates LALMs from three complementary perspectives: instruction following (including speech-level control beyond text commands), knowledge understanding (general knowledge, reasoning, and daily dialogue), and robustness (stability under perturbations in content, environment, and speaker traits). Experiments on representative LALMs reveal notable performance gaps and highlight future directions for improvement. VCB Bench provides a reproducible and fine-grained evaluation framework, offering standardized methodology and practical insights for advancing Chinese voice conversational models.
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            <a href="https://www.alphaxiv.org/abs/2510.11040v1" target="_blank" rel="noopener noreferrer">
                通过工作流对齐任务和基准测试，利用大型语言模型实现以医生为中心的医疗人工智能
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            Enabling Doctor-Centric Medical AI with LLMs through Workflow-Aligned Tasks and Benchmarks
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Wenya Xie, Qingying Xiao, Yu Zheng, Xidong Wang, Junying Chen, Ke Ji, Anningzhe ...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文明确聚焦于医疗AI领域，涉及医生工作流和医疗基准测试，这属于明确的无关主题范畴。论文虽然提到LLMs，但应用场景完全限定在医疗领域，与推荐系统、搜索或广告没有任何潜在关联。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 06:18:27
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11040v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11040v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    The rise of large language models (LLMs) has transformed healthcare by offering clinical guidance, yet their direct deployment to patients poses safety risks due to limited domain expertise. To mitigate this, we propose repositioning LLMs as clinical assistants that collaborate with experienced physicians rather than interacting with patients directly. We conduct a two-stage inspiration-feedback survey to identify real-world needs in clinical workflows. Guided by this, we construct DoctorFLAN, a large-scale Chinese medical dataset comprising 92,000 Q&A instances across 22 clinical tasks and 27 specialties. To evaluate model performance in doctor-facing applications, we introduce DoctorFLAN-test (550 single-turn Q&A items) and DotaBench (74 multi-turn conversations). Experimental results with over ten popular LLMs demonstrate that DoctorFLAN notably improves the performance of open-source LLMs in medical contexts, facilitating their alignment with physician workflows and complementing existing patient-oriented models. This work contributes a valuable resource and framework for advancing doctor-centered medical LLM development
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            <a href="https://www.alphaxiv.org/abs/2510.10998v1" target="_blank" rel="noopener noreferrer">
                ABLEIST：大语言模型生成招聘场景中的交叉性残疾偏见
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            ABLEIST: Intersectional Disability Bias in LLM-Generated Hiring Scenarios
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Mahika Phutane, Hayoung Jung, Matthew Kim, Tanushree Mitra, Aditya Vashistha
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注LLM生成内容中的残疾偏见问题，这属于公平性、伦理等非技术性话题，与我的关注领域无关。论文标题明确指向偏见检测和公平性问题，而非推荐系统、搜索或广告中的技术进展或应用。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 04:18:23
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.10998v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.10998v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CY</span><span class="category-tag">cs.HC</span><span class="category-tag">cs.LG</span></div>
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                    Large language models (LLMs) are increasingly under scrutiny for perpetuating identity-based discrimination in high-stakes domains such as hiring, particularly against people with disabilities (PwD). However, existing research remains largely Western-centric, overlooking how intersecting forms of marginalization--such as gender and caste--shape experiences of PwD in the Global South. We conduct a comprehensive audit of six LLMs across 2,820 hiring scenarios spanning diverse disability, gender, nationality, and caste profiles. To capture subtle intersectional harms and biases, we introduce ABLEIST (Ableism, Inspiration, Superhumanization, and Tokenism), a set of five ableism-specific and three intersectional harm metrics grounded in disability studies literature. Our results reveal significant increases in ABLEIST harms towards disabled candidates--harms that many state-of-the-art models failed to detect. These harms were further amplified by sharp increases in intersectional harms (e.g., Tokenism) for gender and caste-marginalized disabled candidates, highlighting critical blind spots in current safety tools and the need for intersectional safety evaluations of frontier models in high-stakes domains like hiring.
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            <a href="https://www.alphaxiv.org/abs/2510.10994v1" target="_blank" rel="noopener noreferrer">
                DeepResearchGuard：具备开放域评估和多阶段安全护栏的深度研究
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            DeepResearchGuard: Deep Research with Open-Domain Evaluation and Multi-Stage Guardrails for Safety
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Wei-Chieh Huang, Henry Peng Zou, Yaozu Wu, Dongyuan Li, Yankai Chen, Weizhi Zhan...
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题聚焦于安全护栏和评估机制，这属于被明确排除的隐私、安全、伦理等非技术性话题范畴。论文内容似乎主要关注研究过程中的安全保障，与推荐系统、搜索、广告的核心技术进展或使能技术没有任何直接关联。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 04:11:21
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                <a href="https://arxiv.org/abs/2510.10994v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.10994v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                    Deep research frameworks have shown promising capabilities in synthesizing comprehensive reports from web sources. While deep research possesses significant potential to address complex issues through planning and research cycles, existing frameworks are deficient in sufficient evaluation procedures and stage-specific protections. They typically treat evaluation as exact match accuracy of question-answering, but overlook crucial aspects of report quality such as credibility, coherence, breadth, depth, and safety. This oversight may result in hazardous or malicious sources being integrated into the final report. To address these issues, we introduce DEEPRESEARCHGUARD, a comprehensive framework featuring four-stage safeguards with open-domain evaluation of references and reports. We assess performance across multiple metrics, e.g., defense success rate and over-refusal rate, and five key report dimensions. In the absence of a suitable safety benchmark, we introduce DRSAFEBENCH, a stage-wise benchmark for deep research safety. Our evaluation spans diverse state-of-the-art LLMs, including GPT-4o, Gemini-2.5-flash, DeepSeek-v3, and o4-mini. DEEPRESEARCHGUARD achieves an average defense success rate improvement of 18.16% while reducing over-refusal rate by 6%. The input guard provides the most substantial early-stage protection by filtering out obvious risks, while the plan and research guards enhance citation discipline and source credibility. Through extensive experiments, we show that DEEPRESEARCHGUARD enables comprehensive open-domain evaluation and stage-aware defenses that effectively block harmful content propagation, while systematically improving report quality without excessive over-refusal rates. The code can be found via https://github.com/Jasonya/DeepResearchGuard.
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            <a href="https://www.alphaxiv.org/abs/2510.10990v1" target="_blank" rel="noopener noreferrer">
                用于差分隐私合成文本生成的秘密保护演化
            </a>
        </h3>
        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>1/10
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Secret-Protected Evolution for Differentially Private Synthetic Text Generation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Tianze Wang, Zhaoyu Chen, Jian Du, Yingtai Xiao, Linjun Zhang, Qiang Yan
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文涉及差分隐私技术，这属于隐私保护范畴，在无关主题中明确排除。虽然提到了文本生成，但核心焦点是隐私保护机制而非LLM技术本身或其应用。没有证据表明该工作与推荐系统、搜索或广告有直接关联。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 04:05:42
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.10990v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.10990v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CR</span><span class="category-tag">cs.CL</span><span class="category-tag">cs.NE</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Text data has become extremely valuable on large language models (LLMs) and even lead to general artificial intelligence (AGI). A lot of high-quality text in the real world is private and cannot be freely used due to privacy concerns. Therefore, differentially private (DP) synthetic text generation has been proposed, aiming to produce high-utility synthetic data while protecting sensitive information. However, existing DP synthetic text generation imposes uniform guarantees that often overprotect non-sensitive content, resulting in substantial utility loss and computational overhead. Therefore, we propose Secret-Protected Evolution (SecPE), a novel framework that extends private evolution with secret-aware protection. Theoretically, we show that SecPE satisfies $(\mathrm{p}, \mathrm{r})$-secret protection, constituting a relaxation of Gaussian DP that enables tighter utility-privacy trade-offs, while also substantially reducing computational complexity relative to baseline methods. Empirically, across the OpenReview, PubMed, and Yelp benchmarks, SecPE consistently achieves lower Fr\'echet Inception Distance (FID) and higher downstream task accuracy than GDP-based Aug-PE baselines, while requiring less noise to attain the same level of protection. Our results highlight that secret-aware guarantees can unlock more practical and effective privacy-preserving synthetic text generation.
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            <a href="https://www.alphaxiv.org/abs/2510.10961v1" target="_blank" rel="noopener noreferrer">
                KOTOX：用于反混淆和去毒化的韩语毒性数据集
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        <div class="mb-2 text-base text-gray-700">
            KOTOX: A Korean Toxic Dataset for Deobfuscation and Detoxification
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yejin Lee, Su-Hyeon Kim, Hyundong Jin, Dayoung Kim, Yeonsoo Kim, Yo-Sub Han
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于韩语毒性数据集构建，属于内容安全和伦理审查范畴，与推荐系统、搜索或广告的核心技术进展无关。论文主题涉及毒性检测和去毒化，这些属于被明确排除的伦理和内容安全领域，没有任何潜在应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 03:12:37
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.10961v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.10961v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span><span class="category-tag">68T50</span><span class="category-tag">I.2.7</span></div>
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                    Toxic content has become an increasingly critical social issue with the rapid expansion of online communication. While numerous studies explored methods for detecting and detoxifying such content, most have focused primarily on English, leaving low-resource language underrepresented. Consequently, Large Language Models~(LLMs) often struggle to identify and neutralize toxic expressions in these languages. This challenge becomes even more pronounced when user employ obfuscation techniques to evade detection systems. Therefore, we propose a \textbf{KOTOX: Korean Toxic Dataset} for deobfuscation and detoxicification to address this issue. We categorize various obfuscation approaches based on linguistic characteristics of Korean and define a set of transformation rules grounded in real-word examples. Using these rules, we construct three dataset versions (easy, normal, and hard) representing different levels of obfuscation difficulty. This is the first dataset that simultaneously supports deobfuscation and detoxification for the Korean language. We expect it to facilitate better understanding and mitigating of obfuscated toxic content in LLM for low-resource languages. Our code and data are available at https://github.com/leeyejin1231/KOTOX.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.10951v1" target="_blank" rel="noopener noreferrer">
                标点感知的树库树二值化
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            Punctuation-aware treebank tree binarization
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Eitan Klinger, Vivaan Wadhwa, Jungyeul Park
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于句法树二值化这一特定NLP预处理技术，属于纯粹的句法分析领域。虽然涉及树结构处理，但标点感知的树库转换与推荐系统、搜索或广告的核心技术需求没有直接关联，也没有明显的Transformer架构改进或LLM应用潜力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 03:02:38
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.10951v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.10951v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    This article presents a curated resource and evaluation suite for punctuation-aware treebank binarization. Standard binarization pipelines drop punctuation before head selection, which alters constituent shape and harms head-child identification. We release (1) a reproducible pipeline that preserves punctuation as sibling nodes prior to binarization, (2) derived artifacts and metadata (intermediate @X markers, reversibility signatures, alignment indices), and (3) an accompanying evaluation suite covering head-child prediction, round-trip reversibility, and structural compatibility with derivational resources (CCGbank). On the Penn Treebank, punctuation-aware preprocessing improves head prediction accuracy from 73.66\% (Collins rules) and 86.66\% (MLP) to 91.85\% with the same classifier, and achieves competitive alignment against CCGbank derivations. All code, configuration files, and documentation are released to enable replication and extension to other corpora.
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            <a href="https://www.alphaxiv.org/abs/2510.10943v1" target="_blank" rel="noopener noreferrer">
                智能的社会成本：多智能体系统中刻板偏见的涌现、传播与放大
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            The Social Cost of Intelligence: Emergence, Propagation, and Amplification of Stereotypical Bias in Multi-Agent Systems
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Thi-Nhung Nguyen, Linhao Luo, Thuy-Trang Vu, Dinh Phung
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要研究多智能体系统中的偏见问题，属于公平性、伦理等非技术性话题，这些已被明确列为无关主题。虽然多智能体系统在理论上可能与推荐系统相关，但该论文聚焦于社会成本和偏见放大，缺乏对推荐、搜索或广告领域的技术应用潜力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 02:56:42
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.10943v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.10943v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.MA</span><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Bias in large language models (LLMs) remains a persistent challenge, manifesting in stereotyping and unfair treatment across social groups. While prior research has primarily focused on individual models, the rise of multi-agent systems (MAS), where multiple LLMs collaborate and communicate, introduces new and largely unexplored dynamics in bias emergence and propagation. In this work, we present a comprehensive study of stereotypical bias in MAS, examining how internal specialization, underlying LLMs and inter-agent communication protocols influence bias robustness, propagation, and amplification. We simulate social contexts where agents represent different social groups and evaluate system behavior under various interaction and adversarial scenarios. Experiments on three bias benchmarks reveal that MAS are generally less robust than single-agent systems, with bias often emerging early through in-group favoritism. However, cooperative and debate-based communication can mitigate bias amplification, while more robust underlying LLMs improve overall system stability. Our findings highlight critical factors shaping fairness and resilience in multi-agent LLM systems.
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            <a href="https://www.alphaxiv.org/abs/2510.10930v1" target="_blank" rel="noopener noreferrer">
                评估语言模型对游戏的评估能力
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            Evaluating Language Models' Evaluations of Games
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Katherine M. Collins, Cedegao E. Zhang, Graham Todd, Lance Ying, Mauricio Barba ...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题聚焦于语言模型在游戏领域的评估能力，这属于纯粹的LLM评估基准研究，与推荐系统、搜索或广告的核心技术进展无关。论文内容可能涉及LLM在游戏场景下的表现评估，但没有显示出在RecSys/Search/Ads领域的潜在应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 02:45:37
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.10930v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.10930v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                    Reasoning is not just about solving problems -- it is also about evaluating which problems are worth solving at all. Evaluations of artificial intelligence (AI) systems primarily focused on problem solving, historically by studying how models play games such as chess and Go. In this paper, we advocate for a new paradigm that assesses AI systems' evaluation of games. First, we introduce a formalism for evaluating such evaluations. We then leverage a large-scale dataset of over $100$ novel board games and over 450 human judgments to compare evaluations produced by modern language and reasoning models against those of people and symbolic computational agents. We consider two kinds of evaluative queries: assessing the payoff (or fairness) and the funness of games. These queries span two dimensions relevant to the design of evaluations of AI evaluations: how complex a query is to compute and how difficult a query is to quantify. Our results show that reasoning models are generally more aligned to people in their evaluations of games than non-reasoning language models. However, we observe a non-monotonic relationship: as models get closer to game-theoretic optimal, their fit to human data weakens. We also observe more "jaggedness" across models for assessing funness, in line with the greater difficulty of quantifying this query. Across queries and games, reasoning models show highly variable and unpredictable resource usage when assessing queries, pointing to the importance of imbuing more resource-rational meta-reasoning in language and reasoning models.
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            <a href="https://www.alphaxiv.org/abs/2510.10927v1" target="_blank" rel="noopener noreferrer">
                GapDNER：一种用于不连续命名实体识别的间隙感知网格标注模型
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            GapDNER: A Gap-Aware Grid Tagging Model for Discontinuous Named Entity Recognition
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yawen Yang, Fukun Ma, Shiao Meng, Aiwei Liu, Lijie Wen
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于不连续命名实体识别这一特定NLP任务，属于纯粹的序列标注技术。虽然命名实体识别在通用NLP中有广泛应用，但该工作没有展示与推荐系统、搜索或广告领域的直接关联，也不涉及Transformer架构改进、多模态建模或LLM技术等核心关注领域。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 02:40:14
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.10927v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.10927v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    In biomedical fields, one named entity may consist of a series of non-adjacent tokens and overlap with other entities. Previous methods recognize discontinuous entities by connecting entity fragments or internal tokens, which face challenges of error propagation and decoding ambiguity due to the wide variety of span or word combinations. To address these issues, we deeply explore discontinuous entity structures and propose an effective Gap-aware grid tagging model for Discontinuous Named Entity Recognition, named GapDNER. Our GapDNER innovatively applies representation learning on the context gaps between entity fragments to resolve decoding ambiguity and enhance discontinuous NER performance. Specifically, we treat the context gap as an additional type of span and convert span classification into a token-pair grid tagging task. Subsequently, we design two interactive components to comprehensively model token-pair grid features from both intra- and inter-span perspectives. The intra-span regularity extraction module employs the biaffine mechanism along with linear attention to capture the internal regularity of each span, while the inter-span relation enhancement module utilizes criss-cross attention to obtain semantic relations among different spans. At the inference stage of entity decoding, we assign a directed edge to each entity fragment and context gap, then use the BFS algorithm to search for all valid paths from the head to tail of grids with entity tags. Experimental results on three datasets demonstrate that our GapDNER achieves new state-of-the-art performance on discontinuous NER and exhibits remarkable advantages in recognizing complex entity structures.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.11717v1" target="_blank" rel="noopener noreferrer">
                Ev4DGS：基于单目事件流的非刚性物体新视角渲染
            </a>
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            Ev4DGS: Novel-view Rendering of Non-Rigid Objects from Monocular Event Streams
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Takuya Nakabayashi, Navami Kairanda, Hideo Saito, Vladislav Golyanik
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉中的事件相机和3D渲染技术，涉及非刚性物体重建和新视角生成。这些内容与推荐系统、搜索或广告的核心技术领域没有直接关联，也不涉及LLM、Transformer架构或异构数据建模等当前关注的技术方向。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 17:59:55
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11717v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11717v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Event cameras offer various advantages for novel view rendering compared to synchronously operating RGB cameras, and efficient event-based techniques supporting rigid scenes have been recently demonstrated in the literature. In the case of non-rigid objects, however, existing approaches additionally require sparse RGB inputs, which can be a substantial practical limitation; it remains unknown if similar models could be learned from event streams only. This paper sheds light on this challenging open question and introduces Ev4DGS, i.e., the first approach for novel view rendering of non-rigidly deforming objects in the explicit observation space (i.e., as RGB or greyscale images) from monocular event streams. Our method regresses a deformable 3D Gaussian Splatting representation through 1) a loss relating the outputs of the estimated model with the 2D event observation space, and 2) a coarse 3D deformation model trained from binary masks generated from events. We perform experimental comparisons on existing synthetic and newly recorded real datasets with non-rigid objects. The results demonstrate the validity of Ev4DGS and its superior performance compared to multiple naive baselines that can be applied in our setting. We will release our models and the datasets used in the evaluation for research purposes; see the project webpage: https://4dqv.mpi-inf.mpg.de/Ev4DGS/.
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            <a href="https://www.alphaxiv.org/abs/2510.11715v1" target="_blank" rel="noopener noreferrer">
                点提示：使用视频扩散模型进行反事实追踪
            </a>
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            <i class="fa fa-star mr-1"></i>1/10
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            Point Prompting: Counterfactual Tracking with Video Diffusion Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Ayush Shrivastava, Sanyam Mehta, Daniel Geng, Andrew Owens
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">这篇论文专注于视频扩散模型和反事实追踪，属于纯粹的视觉领域研究。虽然反事实分析在推荐系统中可能有应用，但该论文的技术核心是视频处理和生成，没有明确展示与推荐系统、搜索或广告的潜在联系。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 17:59:46
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11715v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11715v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Trackers and video generators solve closely related problems: the former analyze motion, while the latter synthesize it. We show that this connection enables pretrained video diffusion models to perform zero-shot point tracking by simply prompting them to visually mark points as they move over time. We place a distinctively colored marker at the query point, then regenerate the rest of the video from an intermediate noise level. This propagates the marker across frames, tracing the point's trajectory. To ensure that the marker remains visible in this counterfactual generation, despite such markers being unlikely in natural videos, we use the unedited initial frame as a negative prompt. Through experiments with multiple image-conditioned video diffusion models, we find that these "emergent" tracks outperform those of prior zero-shot methods and persist through occlusions, often obtaining performance that is competitive with specialized self-supervised models.
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            <a href="https://www.alphaxiv.org/abs/2510.11712v1" target="_blank" rel="noopener noreferrer">
                DiT360：通过混合训练实现高保真全景图像生成
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            DiT360: High-Fidelity Panoramic Image Generation via Hybrid Training
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Haoran Feng, Dizhe Zhang, Xiangtai Li, Bo Du, Lu Qi
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于全景图像生成技术，属于纯粹的视觉内容生成领域。虽然标题中提到混合训练方法，但这与推荐系统、搜索或广告的核心技术需求没有直接关联，也不涉及LLM技术或Transformer架构在推荐领域的应用。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 17:59:15
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11712v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11712v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    In this work, we propose DiT360, a DiT-based framework that performs hybrid training on perspective and panoramic data for panoramic image generation. For the issues of maintaining geometric fidelity and photorealism in generation quality, we attribute the main reason to the lack of large-scale, high-quality, real-world panoramic data, where such a data-centric view differs from prior methods that focus on model design. Basically, DiT360 has several key modules for inter-domain transformation and intra-domain augmentation, applied at both the pre-VAE image level and the post-VAE token level. At the image level, we incorporate cross-domain knowledge through perspective image guidance and panoramic refinement, which enhance perceptual quality while regularizing diversity and photorealism. At the token level, hybrid supervision is applied across multiple modules, which include circular padding for boundary continuity, yaw loss for rotational robustness, and cube loss for distortion awareness. Extensive experiments on text-to-panorama, inpainting, and outpainting tasks demonstrate that our method achieves better boundary consistency and image fidelity across eleven quantitative metrics. Our code is available at https://github.com/Insta360-Research-Team/DiT360.
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            <a href="https://www.alphaxiv.org/abs/2510.11649v1" target="_blank" rel="noopener noreferrer">
                PhySIC：从单张图像生成物理上合理的3D人-场景交互与接触
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            PhySIC: Physically Plausible 3D Human-Scene Interaction and Contact from a Single Image
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Pradyumna Yalandur Muralidhar, Yuxuan Xue, Xianghui Xie, Margaret Kostyrko, Gera...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉中的3D人体与场景交互重建，属于纯粹的视觉领域研究。虽然涉及交互建模，但缺乏与推荐系统、搜索或广告领域的直接关联，也没有展示出在异构数据处理或Transformer架构方面的创新潜力。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 17:29:51
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11649v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11649v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Reconstructing metrically accurate humans and their surrounding scenes from a single image is crucial for virtual reality, robotics, and comprehensive 3D scene understanding. However, existing methods struggle with depth ambiguity, occlusions, and physically inconsistent contacts. To address these challenges, we introduce PhySIC, a framework for physically plausible Human-Scene Interaction and Contact reconstruction. PhySIC recovers metrically consistent SMPL-X human meshes, dense scene surfaces, and vertex-level contact maps within a shared coordinate frame from a single RGB image. Starting from coarse monocular depth and body estimates, PhySIC performs occlusion-aware inpainting, fuses visible depth with unscaled geometry for a robust metric scaffold, and synthesizes missing support surfaces like floors. A confidence-weighted optimization refines body pose, camera parameters, and global scale by jointly enforcing depth alignment, contact priors, interpenetration avoidance, and 2D reprojection consistency. Explicit occlusion masking safeguards invisible regions against implausible configurations. PhySIC is efficient, requiring only 9 seconds for joint human-scene optimization and under 27 seconds end-to-end. It naturally handles multiple humans, enabling reconstruction of diverse interactions. Empirically, PhySIC outperforms single-image baselines, reducing mean per-vertex scene error from 641 mm to 227 mm, halving PA-MPJPE to 42 mm, and improving contact F1 from 0.09 to 0.51. Qualitative results show realistic foot-floor interactions, natural seating, and plausible reconstructions of heavily occluded furniture. By converting a single image into a physically plausible 3D human-scene pair, PhySIC advances scalable 3D scene understanding. Our implementation is publicly available at https://yuxuan-xue.com/physic.
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            <a href="https://www.alphaxiv.org/abs/2510.11647v1" target="_blank" rel="noopener noreferrer">
                IVEBench：面向指令引导视频编辑评估的现代基准套件
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            IVEBench: Modern Benchmark Suite for Instruction-Guided Video Editing Assessment
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yinan Chen, Jiangning Zhang, Teng Hu, Yuxiang Zeng, Zhucun Xue, Qingdong He, Che...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于视频编辑评估基准，属于纯粹的视觉内容生成领域。虽然涉及指令引导，但核心关注视频编辑这一特定任务，与推荐系统、搜索或广告的排名和建模需求没有直接关联。该基准不涉及用户行为序列、上下文特征建模或Transformer架构效率等与当前关注点相关的技术。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 17:27:08
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11647v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11647v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Instruction-guided video editing has emerged as a rapidly advancing research direction, offering new opportunities for intuitive content transformation while also posing significant challenges for systematic evaluation. Existing video editing benchmarks fail to support the evaluation of instruction-guided video editing adequately and further suffer from limited source diversity, narrow task coverage and incomplete evaluation metrics. To address the above limitations, we introduce IVEBench, a modern benchmark suite specifically designed for instruction-guided video editing assessment. IVEBench comprises a diverse database of 600 high-quality source videos, spanning seven semantic dimensions, and covering video lengths ranging from 32 to 1,024 frames. It further includes 8 categories of editing tasks with 35 subcategories, whose prompts are generated and refined through large language models and expert review. Crucially, IVEBench establishes a three-dimensional evaluation protocol encompassing video quality, instruction compliance and video fidelity, integrating both traditional metrics and multimodal large language model-based assessments. Extensive experiments demonstrate the effectiveness of IVEBench in benchmarking state-of-the-art instruction-guided video editing methods, showing its ability to provide comprehensive and human-aligned evaluation outcomes.
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            <a href="https://www.alphaxiv.org/abs/2510.11631v1" target="_blank" rel="noopener noreferrer">
                EvoCAD：基于视觉语言模型的进化式CAD代码生成
            </a>
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            EvoCAD: Evolutionary CAD Code Generation with Vision Language Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Tobias Preintner, Weixuan Yuan, Adrian König, Thomas Bäck, Elena Raponi, Niki va...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于CAD代码生成，属于计算机辅助设计领域，与推荐系统、搜索或广告的核心技术无关。虽然涉及视觉语言模型，但其应用场景是工程设计而非用户行为建模或内容理解，在推荐/搜索/广告领域没有明显的应用潜力。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 17:12:02
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11631v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11631v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.NE</span></div>
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                    Combining large language models with evolutionary computation algorithms represents a promising research direction leveraging the remarkable generative and in-context learning capabilities of LLMs with the strengths of evolutionary algorithms. In this work, we present EvoCAD, a method for generating computer-aided design (CAD) objects through their symbolic representations using vision language models and evolutionary optimization. Our method samples multiple CAD objects, which are then optimized using an evolutionary approach with vision language and reasoning language models. We assess our method using GPT-4V and GPT-4o, evaluating it on the CADPrompt benchmark dataset and comparing it to prior methods. Additionally, we introduce two new metrics based on topological properties defined by the Euler characteristic, which capture a form of semantic similarity between 3D objects. Our results demonstrate that EvoCAD outperforms previous approaches on multiple metrics, particularly in generating topologically correct objects, which can be efficiently evaluated using our two novel metrics that complement existing spatial metrics.
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            <a href="https://www.alphaxiv.org/abs/2510.11613v1" target="_blank" rel="noopener noreferrer">
                实时高分辨率照片增强：拉普拉斯金字塔网络
            </a>
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            High-resolution Photo Enhancement in Real-time: A Laplacian Pyramid Network
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Feng Zhang, Haoyou Deng, Zhiqiang Li, Lida Li, Bin Xu, Qingbo Lu, Zisheng Cao, M...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉领域的图像增强技术，涉及拉普拉斯金字塔网络用于实时照片处理。这与我的关注点完全无关，因为该技术纯粹属于视觉增强领域，没有明确的推荐系统、搜索或广告应用潜力，也不涉及LLM、Transformer架构或异构数据建模。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 16:52:32
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11613v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11613v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Photo enhancement plays a crucial role in augmenting the visual aesthetics of a photograph. In recent years, photo enhancement methods have either focused on enhancement performance, producing powerful models that cannot be deployed on edge devices, or prioritized computational efficiency, resulting in inadequate performance for real-world applications. To this end, this paper introduces a pyramid network called LLF-LUT++, which integrates global and local operators through closed-form Laplacian pyramid decomposition and reconstruction. This approach enables fast processing of high-resolution images while also achieving excellent performance. Specifically, we utilize an image-adaptive 3D LUT that capitalizes on the global tonal characteristics of downsampled images, while incorporating two distinct weight fusion strategies to achieve coarse global image enhancement. To implement this strategy, we designed a spatial-frequency transformer weight predictor that effectively extracts the desired distinct weights by leveraging frequency features. Additionally, we apply local Laplacian filters to adaptively refine edge details in high-frequency components. After meticulously redesigning the network structure and transformer model, LLF-LUT++ not only achieves a 2.64 dB improvement in PSNR on the HDR+ dataset, but also further reduces runtime, with 4K resolution images processed in just 13 ms on a single GPU. Extensive experimental results on two benchmark datasets further show that the proposed approach performs favorably compared to state-of-the-art methods. The source code will be made publicly available at https://github.com/fengzhang427/LLF-LUT.
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            <a href="https://www.alphaxiv.org/abs/2510.11606v1" target="_blank" rel="noopener noreferrer">
                ExpVid：一个用于实验视频理解与推理的基准
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            ExpVid: A Benchmark for Experiment Video Understanding & Reasoning
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yicheng Xu, Yue Wu, Jiashuo Yu, Ziang Yan, Tianxiang Jiang, Yinan He, Qingsong Z...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于实验视频理解基准，属于纯粹的视觉领域研究，与推荐系统、搜索或广告的核心技术无关。虽然视频理解技术在某些边缘场景可能有潜在应用，但该论文明确针对实验视频这一特定领域，缺乏与RecSys/Search/Ads的直接关联。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 16:45:28
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11606v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11606v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Multimodal Large Language Models (MLLMs) hold promise for accelerating scientific discovery by interpreting complex experimental procedures. However, their true capabilities are poorly understood, as existing benchmarks neglect the fine-grained and long-horizon nature of authentic laboratory work, especially in wet-lab settings. To bridge this gap, we introduce ExpVid, the first benchmark designed to systematically evaluate MLLMs on scientific experiment videos. Curated from peer-reviewed video publications, ExpVid features a new three-level task hierarchy that mirrors the scientific process: (1) Fine-grained Perception of tools, materials, and actions; (2) Procedural Understanding of step order and completeness; and (3) Scientific Reasoning that connects the full experiment to its published conclusions. Our vision-centric annotation pipeline, combining automated generation with multi-disciplinary expert validation, ensures that tasks require visual grounding. We evaluate 19 leading MLLMs on ExpVid and find that while they excel at coarse-grained recognition, they struggle with disambiguating fine details, tracking state changes over time, and linking experimental procedures to scientific outcomes. Our results reveal a notable performance gap between proprietary and open-source models, particularly in high-order reasoning. ExpVid not only provides a diagnostic tool but also charts a roadmap for developing MLLMs capable of becoming trustworthy partners in scientific experimentation.
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            <a href="https://www.alphaxiv.org/abs/2510.11605v1" target="_blank" rel="noopener noreferrer">
                ACE-G：通过查询预训练提升场景坐标回归的泛化能力
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            ACE-G: Improving Generalization of Scene Coordinate Regression Through Query Pre-Training
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Leonard Bruns, Axel Barroso-Laguna, Tommaso Cavallari, Áron Monszpart, Sowmya Mu...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">这篇论文专注于计算机视觉中的场景坐标回归问题，属于纯粹的视觉定位和3D场景理解领域。虽然标题提到泛化改进，但该技术主要应用于机器人导航、增强现实等视觉任务，与推荐系统、搜索或广告的核心技术栈没有直接关联，也没有明显的Transformer架构或LLM应用潜力。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 16:45:17
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11605v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11605v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Scene coordinate regression (SCR) has established itself as a promising learning-based approach to visual relocalization. After mere minutes of scene-specific training, SCR models estimate camera poses of query images with high accuracy. Still, SCR methods fall short of the generalization capabilities of more classical feature-matching approaches. When imaging conditions of query images, such as lighting or viewpoint, are too different from the training views, SCR models fail. Failing to generalize is an inherent limitation of previous SCR frameworks, since their training objective is to encode the training views in the weights of the coordinate regressor itself. The regressor essentially overfits to the training views, by design. We propose to separate the coordinate regressor and the map representation into a generic transformer and a scene-specific map code. This separation allows us to pre-train the transformer on tens of thousands of scenes. More importantly, it allows us to train the transformer to generalize from mapping images to unseen query images during pre-training. We demonstrate on multiple challenging relocalization datasets that our method, ACE-G, leads to significantly increased robustness while keeping the computational footprint attractive.
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            <a href="https://www.alphaxiv.org/abs/2510.11576v1" target="_blank" rel="noopener noreferrer">
                基准测试用于高光谱图像分类的基础模型：在谷物作物类型映射中的应用
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            Benchmarking foundation models for hyperspectral image classification: Application to cereal crop type mapping
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Walid Elbarz, Mohamed Bourriz, Hicham Hajji, Hamd Ait Abdelali, François Bourzei...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于高光谱图像分类和作物类型映射，属于纯粹的视觉/遥感应用领域，与推荐系统、搜索或广告没有直接关联。基础模型基准测试虽然涉及模型评估，但应用场景是农业遥感，完全超出了当前关注的技术范畴。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 16:21:59
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11576v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11576v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Foundation models are transforming Earth observation, but their potential for hyperspectral crop mapping remains underexplored. This study benchmarks three foundation models for cereal crop mapping using hyperspectral imagery: HyperSigma, DOFA, and Vision Transformers pre-trained on the SpectralEarth dataset (a large multitemporal hyperspectral archive). Models were fine-tuned on manually labeled data from a training region and evaluated on an independent test region. Performance was measured with overall accuracy (OA), average accuracy (AA), and F1-score. HyperSigma achieved an OA of 34.5% (+/- 1.8%), DOFA reached 62.6% (+/- 3.5%), and the SpectralEarth model achieved an OA of 93.5% (+/- 0.8%). A compact SpectralEarth variant trained from scratch achieved 91%, highlighting the importance of model architecture for strong generalization across geographic regions and sensor platforms. These results provide a systematic evaluation of foundation models for operational hyperspectral crop mapping and outline directions for future model development.
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            <a href="https://www.alphaxiv.org/abs/2510.11567v1" target="_blank" rel="noopener noreferrer">
                面向城市语义分割的低成本训练数据生成框架
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        <div class="mb-2 text-base text-gray-700">
            A Framework for Low-Effort Training Data Generation for Urban Semantic Segmentation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Denis Zavadski, Damjan Kalšan, Tim Küchler, Haebom Lee, Stefan Roth, Carsten Rot...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉领域的语义分割任务，特别是城市场景的应用。这属于纯粹的视觉研究范畴，与推荐系统、搜索或广告的核心技术没有直接关联。论文内容涉及训练数据生成方法，但未体现任何与LLM、Transformer架构或异构数据建模相关的技术，无法应用于RecSys/Search/Ads领域。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 16:12:29
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11567v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11567v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.GR</span><span class="category-tag">cs.LG</span></div>
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                    Synthetic datasets are widely used for training urban scene recognition models, but even highly realistic renderings show a noticeable gap to real imagery. This gap is particularly pronounced when adapting to a specific target domain, such as Cityscapes, where differences in architecture, vegetation, object appearance, and camera characteristics limit downstream performance. Closing this gap with more detailed 3D modelling would require expensive asset and scene design, defeating the purpose of low-cost labelled data. To address this, we present a new framework that adapts an off-the-shelf diffusion model to a target domain using only imperfect pseudo-labels. Once trained, it generates high-fidelity, target-aligned images from semantic maps of any synthetic dataset, including low-effort sources created in hours rather than months. The method filters suboptimal generations, rectifies image-label misalignments, and standardises semantics across datasets, transforming weak synthetic data into competitive real-domain training sets. Experiments on five synthetic datasets and two real target datasets show segmentation gains of up to +8.0%pt. mIoU over state-of-the-art translation methods, making rapidly constructed synthetic datasets as effective as high-effort, time-intensive synthetic datasets requiring extensive manual design. This work highlights a valuable collaborative paradigm where fast semantic prototyping, combined with generative models, enables scalable, high-quality training data creation for urban scene understanding.
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            <a href="https://www.alphaxiv.org/abs/2510.11566v1" target="_blank" rel="noopener noreferrer">
                SCOOP'D：通过模拟到现实的生成策略学习混合液固物质的舀取
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            SCOOP'D: Learning Mixed-Liquid-Solid Scooping via Sim2Real Generative Policy
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Kuanning Wang, Yongchong Gu, Yuqian Fu, Zeyu Shangguan, Sicheng He, Xiangyang Xu...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">这篇论文专注于机器人物理操作和模拟到现实的迁移学习，涉及混合液固物质的舀取任务。这与推荐系统、搜索、广告或相关使能技术没有任何明显关联，完全超出了当前关注的技术领域范围。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 16:11:34
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11566v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11566v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.RO</span><span class="category-tag">cs.CV</span></div>
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                    Scooping items with tools such as spoons and ladles is common in daily life, ranging from assistive feeding to retrieving items from environmental disaster sites. However, developing a general and autonomous robotic scooping policy is challenging since it requires reasoning about complex tool-object interactions. Furthermore, scooping often involves manipulating deformable objects, such as granular media or liquids, which is challenging due to their infinite-dimensional configuration spaces and complex dynamics. We propose a method, SCOOP'D, which uses simulation from OmniGibson (built on NVIDIA Omniverse) to collect scooping demonstrations using algorithmic procedures that rely on privileged state information. Then, we use generative policies via diffusion to imitate demonstrations from observational input. We directly apply the learned policy in diverse real-world scenarios, testing its performance on various item quantities, item characteristics, and container types. In zero-shot deployment, our method demonstrates promising results across 465 trials in diverse scenarios, including objects of different difficulty levels that we categorize as "Level 1" and "Level 2." SCOOP'D outperforms all baselines and ablations, suggesting that this is a promising approach to acquiring robotic scooping skills. Project page is at https://scoopdiff.github.io/.
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            <a href="https://www.alphaxiv.org/abs/2510.11553v1" target="_blank" rel="noopener noreferrer">
                给定基础模型，为应用标注多少样本？胸部X光分类研究
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            How many samples to label for an application given a foundation model? Chest X-ray classification study
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Nikolay Nechaev, Evgenia Przhezdzetskaya, Viktor Gombolevskiy, Dmitry Umerenkov,...
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学影像（胸部X光）分类的样本标注问题，这属于医疗领域的特定应用。虽然涉及基础模型，但内容与推荐系统、搜索或广告的核心技术进展、Transformer架构改进或异构数据统一建模完全无关。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 15:53:55
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                <a href="https://arxiv.org/abs/2510.11553v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11553v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">68T07 (Primary) 68T45</span><span class="category-tag">62H30</span><span class="category-tag">62P10 (Secondary)</span></div>
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                    Chest X-ray classification is vital yet resource-intensive, typically demanding extensive annotated data for accurate diagnosis. Foundation models mitigate this reliance, but how many labeled samples are required remains unclear. We systematically evaluate the use of power-law fits to predict the training size necessary for specific ROC-AUC thresholds. Testing multiple pathologies and foundation models, we find XrayCLIP and XraySigLIP achieve strong performance with significantly fewer labeled examples than a ResNet-50 baseline. Importantly, learning curve slopes from just 50 labeled cases accurately forecast final performance plateaus. Our results enable practitioners to minimize annotation costs by labeling only the essential samples for targeted performance.
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            <a href="https://www.alphaxiv.org/abs/2510.11520v1" target="_blank" rel="noopener noreferrer">
                mmWalk：迈向多模态多视角行走辅助
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            mmWalk: Towards Multi-modal Multi-view Walking Assistance
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Kedi Ying, Ruiping Liu, Chongyan Chen, Mingzhe Tao, Hao Shi, Kailun Yang, Jiamin...
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题表明其专注于多模态行走辅助系统，这属于物理辅助设备或医疗康复领域，与推荐系统、搜索或广告的核心技术完全无关。论文内容可能涉及传感器融合、步态分析等医疗/机器人技术，没有任何与LLM、Transformer架构或推荐/搜索/广告系统的潜在应用关联。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 15:25:52
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                <a href="https://arxiv.org/abs/2510.11520v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11520v1
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                    Walking assistance in extreme or complex environments remains a significant challenge for people with blindness or low vision (BLV), largely due to the lack of a holistic scene understanding. Motivated by the real-world needs of the BLV community, we build mmWalk, a simulated multi-modal dataset that integrates multi-view sensor and accessibility-oriented features for outdoor safe navigation. Our dataset comprises 120 manually controlled, scenario-categorized walking trajectories with 62k synchronized frames. It contains over 559k panoramic images across RGB, depth, and semantic modalities. Furthermore, to emphasize real-world relevance, each trajectory involves outdoor corner cases and accessibility-specific landmarks for BLV users. Additionally, we generate mmWalkVQA, a VQA benchmark with over 69k visual question-answer triplets across 9 categories tailored for safe and informed walking assistance. We evaluate state-of-the-art Vision-Language Models (VLMs) using zero- and few-shot settings and found they struggle with our risk assessment and navigational tasks. We validate our mmWalk-finetuned model on real-world datasets and show the effectiveness of our dataset for advancing multi-modal walking assistance.
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            <a href="https://www.alphaxiv.org/abs/2510.11512v1" target="_blank" rel="noopener noreferrer">
                LikePhys：通过似然偏好评估视频扩散模型中的直觉物理理解
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            LikePhys: Evaluating Intuitive Physics Understanding in Video Diffusion Models via Likelihood Preference
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jianhao Yuan, Fabio Pizzati, Francesco Pinto, Lars Kunze, Ivan Laptev, Paul Newm...
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 15:19:07
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                <a href="https://arxiv.org/abs/2510.11512v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11512v1
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                    Intuitive physics understanding in video diffusion models plays an essential role in building general-purpose physically plausible world simulators, yet accurately evaluating such capacity remains a challenging task due to the difficulty in disentangling physics correctness from visual appearance in generation. To the end, we introduce LikePhys, a training-free method that evaluates intuitive physics in video diffusion models by distinguishing physically valid and impossible videos using the denoising objective as an ELBO-based likelihood surrogate on a curated dataset of valid-invalid pairs. By testing on our constructed benchmark of twelve scenarios spanning over four physics domains, we show that our evaluation metric, Plausibility Preference Error (PPE), demonstrates strong alignment with human preference, outperforming state-of-the-art evaluator baselines. We then systematically benchmark intuitive physics understanding in current video diffusion models. Our study further analyses how model design and inference settings affect intuitive physics understanding and highlights domain-specific capacity variations across physical laws. Empirical results show that, despite current models struggling with complex and chaotic dynamics, there is a clear trend of improvement in physics understanding as model capacity and inference settings scale.
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            <a href="https://www.alphaxiv.org/abs/2510.11508v1" target="_blank" rel="noopener noreferrer">
                面向使用连续分量的快速可扩展法向积分
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            Towards Fast and Scalable Normal Integration using Continuous Components
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Francesco Milano, Jen Jen Chung, Lionel Ott, Roland Siegwart
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题涉及计算机视觉中的法向积分技术，主要用于3D重建和表面建模，属于纯粹的视觉处理范畴。没有证据表明该方法在推荐系统、搜索或广告领域有直接应用潜力，也不涉及LLM、Transformer架构或异构数据处理等当前关注的技术方向。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 15:17:16
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                <a href="https://arxiv.org/abs/2510.11508v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11508v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Surface normal integration is a fundamental problem in computer vision, dealing with the objective of reconstructing a surface from its corresponding normal map. Existing approaches require an iterative global optimization to jointly estimate the depth of each pixel, which scales poorly to larger normal maps. In this paper, we address this problem by recasting normal integration as the estimation of relative scales of continuous components. By constraining pixels belonging to the same component to jointly vary their scale, we drastically reduce the number of optimization variables. Our framework includes a heuristic to accurately estimate continuous components from the start, a strategy to rebalance optimization terms, and a technique to iteratively merge components to further reduce the size of the problem. Our method achieves state-of-the-art results on the standard normal integration benchmark in as little as a few seconds and achieves one-order-of-magnitude speedup over pixel-level approaches on large-resolution normal maps.
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            <a href="https://www.alphaxiv.org/abs/2510.11473v1" target="_blank" rel="noopener noreferrer">
                VA-GS：通过视角对齐增强高斯泼溅的几何表示
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            VA-GS: Enhancing the Geometric Representation of Gaussian Splatting via View Alignment
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Qing Li, Huifang Feng, Xun Gong, Yu-Shen Liu
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机图形学中的3D重建技术（高斯泼溅），属于纯粹的视觉领域研究。虽然标题提到几何表示增强，但没有任何内容表明与推荐系统、搜索或广告有直接或间接的潜在应用关联。这完全属于被排除的纯粹视觉论文范畴。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 14:44:50
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                <a href="https://arxiv.org/abs/2510.11473v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11473v1
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                    3D Gaussian Splatting has recently emerged as an efficient solution for high-quality and real-time novel view synthesis. However, its capability for accurate surface reconstruction remains underexplored. Due to the discrete and unstructured nature of Gaussians, supervision based solely on image rendering loss often leads to inaccurate geometry and inconsistent multi-view alignment. In this work, we propose a novel method that enhances the geometric representation of 3D Gaussians through view alignment (VA). Specifically, we incorporate edge-aware image cues into the rendering loss to improve surface boundary delineation. To enforce geometric consistency across views, we introduce a visibility-aware photometric alignment loss that models occlusions and encourages accurate spatial relationships among Gaussians. To further mitigate ambiguities caused by lighting variations, we incorporate normal-based constraints to refine the spatial orientation of Gaussians and improve local surface estimation. Additionally, we leverage deep image feature embeddings to enforce cross-view consistency, enhancing the robustness of the learned geometry under varying viewpoints and illumination. Extensive experiments on standard benchmarks demonstrate that our method achieves state-of-the-art performance in both surface reconstruction and novel view synthesis. The source code is available at https://github.com/LeoQLi/VA-GS.
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                耦合退化建模与融合：基于视觉语言模型的退化耦合网络用于退化感知的红外与可见光图像融合
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            Coupled Degradation Modeling and Fusion: A VLM-Guided Degradation-Coupled Network for Degradation-Aware Infrared and Visible Image Fusion
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Tianpei Zhang, Jufeng Zhao, Yiming Zhu, Guangmang Cui
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于红外与可见光图像融合的计算机视觉任务，属于纯粹的视觉处理领域。虽然提到了VLM（视觉语言模型）作为指导，但核心内容是图像退化建模和融合技术，与推荐系统、搜索或广告领域没有直接关联。论文的技术方向更偏向于多模态视觉处理，而非用户行为建模或内容排序等RecSys/Search/Ads核心问题。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 14:26:33
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                <a href="https://arxiv.org/abs/2510.11456v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11456v1
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Existing Infrared and Visible Image Fusion (IVIF) methods typically assume high-quality inputs. However, when handing degraded images, these methods heavily rely on manually switching between different pre-processing techniques. This decoupling of degradation handling and image fusion leads to significant performance degradation. In this paper, we propose a novel VLM-Guided Degradation-Coupled Fusion network (VGDCFusion), which tightly couples degradation modeling with the fusion process and leverages vision-language models (VLMs) for degradation-aware perception and guided suppression. Specifically, the proposed Specific-Prompt Degradation-Coupled Extractor (SPDCE) enables modality-specific degradation awareness and establishes a joint modeling of degradation suppression and intra-modal feature extraction. In parallel, the Joint-Prompt Degradation-Coupled Fusion (JPDCF) facilitates cross-modal degradation perception and couples residual degradation filtering with complementary cross-modal feature fusion. Extensive experiments demonstrate that our VGDCFusion significantly outperforms existing state-of-the-art fusion approaches under various degraded image scenarios. Our code is available at https://github.com/Lmmh058/VGDCFusion.
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            <a href="https://www.alphaxiv.org/abs/2510.11449v1" target="_blank" rel="noopener noreferrer">
                增强内河水域海事领域感知：基于YOLO的卫星与AIS数据融合用于船舶特征识别
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            Enhancing Maritime Domain Awareness on Inland Waterways: A YOLO-Based Fusion of Satellite and AIS for Vessel Characterization
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Geoffery Agorku, Sarah Hernandez, Hayley Hames, Cade Wagner
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于海事领域的计算机视觉应用，使用YOLO进行船舶检测和卫星/AIS数据融合。这与搜索、推荐或广告系统无关，也不涉及LLM、Transformer架构或异质数据统一建模等核心技术。该研究属于纯粹的视觉应用领域，没有明确的RecSys/Search/Ads应用潜力。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 14:19:58
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11449v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11449v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Maritime Domain Awareness (MDA) for inland waterways remains challenged by cooperative system vulnerabilities. This paper presents a novel framework that fuses high-resolution satellite imagery with vessel trajectory data from the Automatic Identification System (AIS). This work addresses the limitations of AIS-based monitoring by leveraging non-cooperative satellite imagery and implementing a fusion approach that links visual detections with AIS data to identify dark vessels, validate cooperative traffic, and support advanced MDA. The You Only Look Once (YOLO) v11 object detection model is used to detect and characterize vessels and barges by vessel type, barge cover, operational status, barge count, and direction of travel. An annotated data set of 4,550 instances was developed from $5{,}973~\mathrm{mi}^2$ of Lower Mississippi River imagery. Evaluation on a held-out test set demonstrated vessel classification (tugboat, crane barge, bulk carrier, cargo ship, and hopper barge) with an F1 score of 95.8\%; barge cover (covered or uncovered) detection yielded an F1 score of 91.6\%; operational status (staged or in motion) classification reached an F1 score of 99.4\%. Directionality (upstream, downstream) yielded 93.8\% accuracy. The barge count estimation resulted in a mean absolute error (MAE) of 2.4 barges. Spatial transferability analysis across geographically disjoint river segments showed accuracy was maintained as high as 98\%. These results underscore the viability of integrating non-cooperative satellite sensing with AIS fusion. This approach enables near-real-time fleet inventories, supports anomaly detection, and generates high-quality data for inland waterway surveillance. Future work will expand annotated datasets, incorporate temporal tracking, and explore multi-modal deep learning to further enhance operational scalability.
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            <a href="https://www.alphaxiv.org/abs/2510.11387v1" target="_blank" rel="noopener noreferrer">
                MaterialRefGS：具有多视角一致材质推断的反射高斯泼溅
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            MaterialRefGS: Reflective Gaussian Splatting with Multi-view Consistent Material Inference
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Wenyuan Zhang, Jimin Tang, Weiqi Zhang, Yi Fang, Yu-Shen Liu, Zhizhong Han
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉中的3D重建和材质推断技术，属于纯粹的视觉领域研究。虽然高斯泼溅是3D表示的新方法，但该工作没有展示与推荐系统、搜索或广告的明显关联，也不涉及Transformer架构、LLM技术或异构数据建模等核心关注领域。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 13:29:20
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11387v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11387v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Modeling reflections from 2D images is essential for photorealistic rendering and novel view synthesis. Recent approaches enhance Gaussian primitives with reflection-related material attributes to enable physically based rendering (PBR) with Gaussian Splatting. However, the material inference often lacks sufficient constraints, especially under limited environment modeling, resulting in illumination aliasing and reduced generalization. In this work, we revisit the problem from a multi-view perspective and show that multi-view consistent material inference with more physically-based environment modeling is key to learning accurate reflections with Gaussian Splatting. To this end, we enforce 2D Gaussians to produce multi-view consistent material maps during deferred shading. We also track photometric variations across views to identify highly reflective regions, which serve as strong priors for reflection strength terms. To handle indirect illumination caused by inter-object occlusions, we further introduce an environment modeling strategy through ray tracing with 2DGS, enabling photorealistic rendering of indirect radiance. Experiments on widely used benchmarks show that our method faithfully recovers both illumination and geometry, achieving state-of-the-art rendering quality in novel views synthesis.
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            <a href="https://www.alphaxiv.org/abs/2510.11369v1" target="_blank" rel="noopener noreferrer">
                推理即表征：重新思考图像质量评估中的视觉强化学习
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            Reasoning as Representation: Rethinking Visual Reinforcement Learning in Image Quality Assessment
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Shijie Zhao, Xuanyu Zhang, Weiqi Li, Junlin Li, Li Zhang, Tianfan Xue, Jian Zhan...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注视觉强化学习在图像质量评估中的应用，这属于纯粹的视觉领域研究，与推荐系统、搜索或广告没有明确关联。论文标题中提到的强化学习也没有展示出在RecSys/Search/Ads领域的直接应用潜力，因此相关性极低。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 13:11:08
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11369v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11369v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Reasoning-based image quality assessment (IQA) models trained through reinforcement learning (RL) exhibit exceptional generalization, yet the underlying mechanisms and critical factors driving this capability remain underexplored in current research. Moreover, despite their superior performance, these models incur inference energy usage and latency orders of magnitude higher than their earlier counterparts, restricting their deployment in specific scenarios. Through extensive experiments, this paper verifies and elaborates that through RL training, MLLMs leverage their reasoning capability to convert redundant visual representations into compact, cross-domain aligned text representations. This conversion is precisely the source of the generalization exhibited by these reasoning-based IQA models. Building on this fundamental insight, we propose a novel algorithm, RALI, which employs contrastive learning to directly align images with these generalizable text representations learned by RL. This approach eliminates the reliance on reasoning processes and even obviates the need to load an LLM. For the quality scoring task, this framework achieves generalization performance comparable to reasoning-based models while requiring less than 5% of their model parameters and inference time.
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            <a href="https://www.alphaxiv.org/abs/2510.11344v1" target="_blank" rel="noopener noreferrer">
                MMAP：一种用于预测空间基因表达的多放大倍数与原型感知架构
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            MMAP: A Multi-Magnification and Prototype-Aware Architecture for Predicting Spatial Gene Expression
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Hai Dang Nguyen, Nguyen Dang Huy Pham, The Minh Duc Nguyen, Dac Thai Nguyen, Han...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于生物医学领域的空间基因表达预测，属于明确的无关主题（医学/生物学应用）。虽然提到了多模态架构，但其应用场景与推荐系统、搜索或广告完全无关，没有任何潜在的应用价值。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 12:41:09
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11344v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11344v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Spatial Transcriptomics (ST) enables the measurement of gene expression while preserving spatial information, offering critical insights into tissue architecture and disease pathology. Recent developments have explored the use of hematoxylin and eosin (H&E)-stained whole-slide images (WSIs) to predict transcriptome-wide gene expression profiles through deep neural networks. This task is commonly framed as a regression problem, where each input corresponds to a localized image patch extracted from the WSI. However, predicting spatial gene expression from histological images remains a challenging problem due to the significant modality gap between visual features and molecular signals. Recent studies have attempted to incorporate both local and global information into predictive models. Nevertheless, existing methods still suffer from two key limitations: (1) insufficient granularity in local feature extraction, and (2) inadequate coverage of global spatial context. In this work, we propose a novel framework, MMAP (Multi-MAgnification and Prototype-enhanced architecture), that addresses both challenges simultaneously. To enhance local feature granularity, MMAP leverages multi-magnification patch representations that capture fine-grained histological details. To improve global contextual understanding, it learns a set of latent prototype embeddings that serve as compact representations of slide-level information. Extensive experimental results demonstrate that MMAP consistently outperforms all existing state-of-the-art methods across multiple evaluation metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), and Pearson Correlation Coefficient (PCC).
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            <a href="https://www.alphaxiv.org/abs/2510.11340v1" target="_blank" rel="noopener noreferrer">
                REACT3D：为交互式物理3D场景恢复关节结构
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            REACT3D: Recovering Articulations for Interactive Physical 3D Scenes
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zhao Huang, Boyang Sun, Alexandros Delitzas, Jiaqi Chen, Marc Pollefeys
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于3D视觉中的关节恢复和物理场景建模，属于纯粹的计算机视觉和图形学领域。虽然标题提到'交互式'，但内容与推荐系统、搜索或广告的核心技术没有任何直接关联，也不涉及LLM、Transformer架构或异构数据建模等关键技术。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 12:37:59
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11340v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11340v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.RO</span></div>
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                    Interactive 3D scenes are increasingly vital for embodied intelligence, yet existing datasets remain limited due to the labor-intensive process of annotating part segmentation, kinematic types, and motion trajectories. We present REACT3D, a scalable zero-shot framework that converts static 3D scenes into simulation-ready interactive replicas with consistent geometry, enabling direct use in diverse downstream tasks. Our contributions include: (i) openable-object detection and segmentation to extract candidate movable parts from static scenes, (ii) articulation estimation that infers joint types and motion parameters, (iii) hidden-geometry completion followed by interactive object assembly, and (iv) interactive scene integration in widely supported formats to ensure compatibility with standard simulation platforms. We achieve state-of-the-art performance on detection/segmentation and articulation metrics across diverse indoor scenes, demonstrating the effectiveness of our framework and providing a practical foundation for scalable interactive scene generation, thereby lowering the barrier to large-scale research on articulated scene understanding. Our project page is \textit{\hypersetup{urlcolor=black}\href{https://react3d.github.io/}{react3d.github.io}}.
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            <a href="https://www.alphaxiv.org/abs/2510.11305v1" target="_blank" rel="noopener noreferrer">
                评估预处理、方法选择和超参数调优对基于合成孔径雷达的洪水制图与水深估算的影响
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            Evaluating the effects of preprocessing, method selection, and hyperparameter tuning on SAR-based flood mapping and water depth estimation
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jean-Paul Travert, Cédric Goeury, Sébastien Boyaval, Vito Bacchi, Fabrice Zaoui
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于遥感领域的SAR（合成孔径雷达）洪水制图和水深估算，属于地球科学和环境监测应用。这与推荐系统、搜索、广告或相关LLM技术没有任何直接或间接关联，完全超出了关注范围。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 11:54:42
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11305v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11305v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">physics.geo-ph</span></div>
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                    Flood mapping and water depth estimation from Synthetic Aperture Radar (SAR) imagery are crucial for calibrating and validating hydraulic models. This study uses SAR imagery to evaluate various preprocessing (especially speckle noise reduction), flood mapping, and water depth estimation methods. The impact of the choice of method at different steps and its hyperparameters is studied by considering an ensemble of preprocessed images, flood maps, and water depth fields. The evaluation is conducted for two flood events on the Garonne River (France) in 2019 and 2021, using hydrodynamic simulations and in-situ observations as reference data. Results show that the choice of speckle filter alters flood extent estimations with variations of several square kilometers. Furthermore, the selection and tuning of flood mapping methods also affect performance. While supervised methods outperformed unsupervised ones, tuned unsupervised approaches (such as local thresholding or change detection) can achieve comparable results. The compounded uncertainty from preprocessing and flood mapping steps also introduces high variability in the water depth field estimates. This study highlights the importance of considering the entire processing pipeline, encompassing preprocessing, flood mapping, and water depth estimation methods and their associated hyperparameters. Rather than relying on a single configuration, adopting an ensemble approach and accounting for methodological uncertainty should be privileged. For flood mapping, the method choice has the most influence. For water depth estimation, the most influential processing step was the flood map input resulting from the flood mapping step and the hyperparameters of the methods.
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            <a href="https://www.alphaxiv.org/abs/2510.11303v1" target="_blank" rel="noopener noreferrer">
                sketch2symm：通过语义桥接实现对称感知的草图到形状生成
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            sketch2symm: Symmetry-aware sketch-to-shape generation via semantic bridging
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yan Zhou, Mingji Li, Xiantao Zeng, Jie Lin, Yuexia Zhou
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉领域的草图到3D形状生成，属于纯粹的视觉生成任务。虽然提到了对称性感知和语义桥接技术，但这些技术主要应用于图形学和3D建模领域，与推荐系统、搜索或广告的核心技术栈没有直接关联，也不涉及Transformer架构或LLM技术的应用。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 11:49:45
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                <a href="https://arxiv.org/abs/2510.11303v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11303v1
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                    Sketch-based 3D reconstruction remains a challenging task due to the abstract and sparse nature of sketch inputs, which often lack sufficient semantic and geometric information. To address this, we propose Sketch2Symm, a two-stage generation method that produces geometrically consistent 3D shapes from sketches. Our approach introduces semantic bridging via sketch-to-image translation to enrich sparse sketch representations, and incorporates symmetry constraints as geometric priors to leverage the structural regularity commonly found in everyday objects. Experiments on mainstream sketch datasets demonstrate that our method achieves superior performance compared to existing sketch-based reconstruction methods in terms of Chamfer Distance, Earth Mover's Distance, and F-Score, verifying the effectiveness of the proposed semantic bridging and symmetry-aware design.
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            <a href="https://www.alphaxiv.org/abs/2510.11287v1" target="_blank" rel="noopener noreferrer">
                EEMS：基于可学习门控机制的边缘提示增强医学图像分割
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            EEMS: Edge-Prompt Enhanced Medical Image Segmentation Based on Learnable Gating Mechanism
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Han Xia, Quanjun Li, Qian Li, Zimeng Li, Hongbin Ye, Yupeng Liu, Haolun Li, Xuha...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学图像分割领域，属于明确的无关主题（医学、生物医学应用）。虽然提到了可学习门控机制等技术概念，但其核心应用场景是医学图像处理，与推荐系统、搜索或广告领域没有任何直接或潜在的关联。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 11:21:57
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11287v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11287v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Medical image segmentation is vital for diagnosis, treatment planning, and disease monitoring but is challenged by complex factors like ambiguous edges and background noise. We introduce EEMS, a new model for segmentation, combining an Edge-Aware Enhancement Unit (EAEU) and a Multi-scale Prompt Generation Unit (MSPGU). EAEU enhances edge perception via multi-frequency feature extraction, accurately defining boundaries. MSPGU integrates high-level semantic and low-level spatial features using a prompt-guided approach, ensuring precise target localization. The Dual-Source Adaptive Gated Fusion Unit (DAGFU) merges edge features from EAEU with semantic features from MSPGU, enhancing segmentation accuracy and robustness. Tests on datasets like ISIC2018 confirm EEMS's superior performance and reliability as a clinical tool.
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            <a href="https://www.alphaxiv.org/abs/2510.11260v1" target="_blank" rel="noopener noreferrer">
                基于大语言模型的扫描电子显微镜图像自动标尺检测与提取框架
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            A Large-Language-Model Assisted Automated Scale Bar Detection and Extraction Framework for Scanning Electron Microscopic Images
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yuxuan Chen, Ruotong Yang, Zhengyang Zhang, Mehreen Ahmed, Yanming Wang
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于扫描电子显微镜图像的标尺检测，属于医学/生物学领域的特定应用。虽然使用了LLM技术，但应用场景与推荐系统、搜索或广告领域完全无关，且没有展示任何在这些领域的潜在应用价值。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 10:50:54
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11260v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11260v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cond-mat.mtrl-sci</span><span class="category-tag">cs.AI</span><span class="category-tag">physics.data-an</span></div>
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                    Microscopic characterizations, such as Scanning Electron Microscopy (SEM), are widely used in scientific research for visualizing and analyzing microstructures. Determining the scale bars is an important first step of accurate SEM analysis; however, currently, it mainly relies on manual operations, which is both time-consuming and prone to errors. To address this issue, we propose a multi-modal and automated scale bar detection and extraction framework that provides concurrent object detection, text detection and text recognition with a Large Language Model (LLM) agent. The proposed framework operates in four phases; i) Automatic Dataset Generation (Auto-DG) model to synthesize a diverse dataset of SEM images ensuring robust training and high generalizability of the model, ii) scale bar object detection, iii) information extraction using a hybrid Optical Character Recognition (OCR) system with DenseNet and Convolutional Recurrent Neural Network (CRNN) based algorithms, iv) an LLM agent to analyze and verify accuracy of the results. The proposed model demonstrates a strong performance in object detection and accurate localization with a precision of 100%, recall of 95.8%, and a mean Average Precision (mAP) of 99.2% at IoU=0.5 and 69.1% at IoU=0.5:0.95. The hybrid OCR system achieved 89% precision, 65% recall, and a 75% F1 score on the Auto-DG dataset, significantly outperforming several mainstream standalone engines, highlighting its reliability for scientific image analysis. The LLM is introduced as a reasoning engine as well as an intelligent assistant that suggests follow-up steps and verifies the results. This automated method powered by an LLM agent significantly enhances the efficiency and accuracy of scale bar detection and extraction in SEM images, providing a valuable tool for microscopic analysis and advancing the field of scientific imaging.
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            <a href="https://www.alphaxiv.org/abs/2510.11259v1" target="_blank" rel="noopener noreferrer">
                DTEA：用于医学图像分割的动态拓扑编织与不稳定性驱动的熵衰减
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            DTEA: Dynamic Topology Weaving and Instability-Driven Entropic Attenuation for Medical Image Segmentation
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Weixuan Li, Quanjun Li, Guang Yu, Song Yang, Zimeng Li, Chi-Man Pun, Yupeng Liu,...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学图像分割领域，属于明确的医学领域特定应用，与搜索、推荐、广告系统完全无关。论文标题中提到的动态拓扑编织和熵衰减技术是计算机视觉领域的专业方法，没有显示出在推荐系统、搜索或广告领域的潜在应用价值。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 10:50:41
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11259v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11259v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    In medical image segmentation, skip connections are used to merge global context and reduce the semantic gap between encoder and decoder. Current methods often struggle with limited structural representation and insufficient contextual modeling, affecting generalization in complex clinical scenarios. We propose the DTEA model, featuring a new skip connection framework with the Semantic Topology Reconfiguration (STR) and Entropic Perturbation Gating (EPG) modules. STR reorganizes multi-scale semantic features into a dynamic hypergraph to better model cross-resolution anatomical dependencies, enhancing structural and semantic representation. EPG assesses channel stability after perturbation and filters high-entropy channels to emphasize clinically important regions and improve spatial attention. Extensive experiments on three benchmark datasets show our framework achieves superior segmentation accuracy and better generalization across various clinical settings. The code is available at \href{https://github.com/LWX-Research/DTEA}{https://github.com/LWX-Research/DTEA}.
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            <a href="https://www.alphaxiv.org/abs/2510.11243v1" target="_blank" rel="noopener noreferrer">
                尼泊尔手语字符识别：数据集开发与深度学习方法
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            Nepali Sign Language Characters Recognition: Dataset Development and Deep Learning Approaches
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Birat Poudel, Satyam Ghimire, Sijan Bhattarai, Saurav Bhandari, Suramya Sharma D...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于特定领域的手语识别，属于计算机视觉应用，与推荐系统、搜索或广告的核心技术领域无关。论文内容涉及数据集开发和深度学习在特定语言识别中的应用，没有显示出与推荐、搜索或广告系统的潜在关联。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 10:29:08
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11243v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11243v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    Sign languages serve as essential communication systems for individuals with hearing and speech impairments. However, digital linguistic dataset resources for underrepresented sign languages, such as Nepali Sign Language (NSL), remain scarce. This study introduces the first benchmark dataset for NSL, consisting of 36 gesture classes with 1,500 samples per class, designed to capture the structural and visual features of the language. To evaluate recognition performance, we fine-tuned MobileNetV2 and ResNet50 architectures on the dataset, achieving classification accuracies of 90.45% and 88.78%, respectively. These findings demonstrate the effectiveness of convolutional neural networks in sign recognition tasks, particularly within low-resource settings. To the best of our knowledge, this work represents the first systematic effort to construct a benchmark dataset and assess deep learning approaches for NSL recognition, highlighting the potential of transfer learning and fine-tuning for advancing research in underexplored sign languages.
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            <a href="https://www.alphaxiv.org/abs/2510.11232v1" target="_blank" rel="noopener noreferrer">
                LightPneumoNet：轻量级肺炎分类器
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            LightPneumoNet: Lightweight Pneumonia Classifier
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Neilansh Chauhan, Piyush Kumar Gupta, Faraz Doja
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确指向医学领域的肺炎分类应用，属于明确的无关主题范畴。虽然提到了轻量级网络设计，但其核心应用场景（医疗诊断）与推荐系统、搜索或广告领域没有任何关联。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 10:14:17
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11232v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11232v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.LG</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Effective pneumonia diagnosis is often challenged by the difficulty of deploying large, computationally expensive deep learning models in resource-limited settings. This study introduces LightPneumoNet, an efficient, lightweight convolutional neural network (CNN) built from scratch to provide an accessible and accurate diagnostic solution for pneumonia detection from chest X-rays. Our model was trained on a public dataset of 5,856 chest X-ray images. Preprocessing included image resizing to 224x224, grayscale conversion, and pixel normalization, with data augmentation (rotation, zoom, shear) to prevent overfitting. The custom architecture features four blocks of stacked convolutional layers and contains only 388,082 trainable parameters, resulting in a minimal 1.48 MB memory footprint. On the independent test set, our model delivered exceptional performance, achieving an overall accuracy of 0.942, precision of 0.92, and an F1-Score of 0.96. Critically, it obtained a sensitivity (recall) of 0.99, demonstrating a near-perfect ability to identify true pneumonia cases and minimize clinically significant false negatives. Notably, LightPneumoNet achieves this high recall on the same dataset where existing approaches typically require significantly heavier architectures or fail to reach comparable sensitivity levels. The model's efficiency enables deployment on low-cost hardware, making advanced computer-aided diagnosis accessible in underserved clinics and serving as a reliable second-opinion tool to improve patient outcomes.
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            <a href="https://www.alphaxiv.org/abs/2510.11183v1" target="_blank" rel="noopener noreferrer">
                使用T5模型进行沙特手语翻译
            </a>
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            Saudi Sign Language Translation Using T5
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Ali Alhejab, Tomas Zelezny, Lamya Alkanhal, Ivan Gruber, Yazeed Alharbi, Jakub S...
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于特定领域的手语翻译任务，属于纯粹的自然语言处理应用，与推荐系统、搜索或广告的核心技术领域没有直接关联。手语翻译作为专门的NLP任务，不涉及推荐算法、搜索排序、广告投放或Transformer架构的效率改进等关键技术方向。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 09:18:34
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11183v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11183v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    This paper explores the application of T5 models for Saudi Sign Language (SSL) translation using a novel dataset. The SSL dataset includes three challenging testing protocols, enabling comprehensive evaluation across different scenarios. Additionally, it captures unique SSL characteristics, such as face coverings, which pose challenges for sign recognition and translation. In our experiments, we investigate the impact of pre-training on American Sign Language (ASL) data by comparing T5 models pre-trained on the YouTubeASL dataset with models trained directly on the SSL dataset. Experimental results demonstrate that pre-training on YouTubeASL significantly improves models' performance (roughly $3\times$ in BLEU-4), indicating cross-linguistic transferability in sign language models. Our findings highlight the benefits of leveraging large-scale ASL data to improve SSL translation and provide insights into the development of more effective sign language translation systems. Our code is publicly available at our GitHub repository.
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            <a href="https://www.alphaxiv.org/abs/2510.11182v1" target="_blank" rel="noopener noreferrer">
                跨多种癌症类型的组织病理学全切片图像中自动肿瘤分割的泛化性研究
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            Generalisation of automatic tumour segmentation in histopathological whole-slide images across multiple cancer types
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Ole-Johan Skrede, Manohar Pradhan, Maria Xepapadakis Isaksen, Tarjei Sveinsgjerd...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学图像分割领域，特别是组织病理学中的肿瘤分割，这属于医学和生物学的具体应用领域。根据用户明确的排除标准，医学、生物学等特定领域应用属于不相关主题，且该研究没有显示出与推荐系统、搜索或广告领域的潜在联系。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 09:18:15
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11182v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11182v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">eess.IV</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CV</span></div>
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                    Deep learning is expected to aid pathologists by automating tasks such as tumour segmentation. We aimed to develop one universal tumour segmentation model for histopathological images and examine its performance in different cancer types. The model was developed using over 20 000 whole-slide images from over 4 000 patients with colorectal, endometrial, lung, or prostate carcinoma. Performance was validated in pre-planned analyses on external cohorts with over 3 000 patients across six cancer types. Exploratory analyses included over 1 500 additional patients from The Cancer Genome Atlas. Average Dice coefficient was over 80% in all validation cohorts with en bloc resection specimens and in The Cancer Genome Atlas cohorts. No loss of performance was observed when comparing the universal model with models specialised on single cancer types. In conclusion, extensive and rigorous evaluations demonstrate that generic tumour segmentation by a single model is possible across cancer types, patient populations, sample preparations, and slide scanners.
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            <a href="https://www.alphaxiv.org/abs/2510.11176v1" target="_blank" rel="noopener noreferrer">
                G2L：通过知识蒸馏从千亿级到癌症特异性大规模病理学基础模型
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            G2L:From Giga-Scale to Cancer-Specific Large-Scale Pathology Foundation Models via Knowledge Distillation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yesung Cho, Sungmin Lee, Geongyu Lee, Minkyung Lee, Jongbae Park, Dongmyung Shin
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学病理学领域的癌症特异性基础模型开发，这属于明确的无关主题（医学/生物学领域特定应用）。尽管提到了知识蒸馏技术，但其核心应用场景与推荐系统、搜索或广告领域完全无关，没有任何潜在的应用价值。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 09:08:59
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11176v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11176v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Recent studies in pathology foundation models have shown that scaling training data, diversifying cancer types, and increasing model size consistently improve their performance. However, giga-scale foundation models, which are trained on hundreds of thousands of slides covering tens of cancer types and contain billions of parameters, pose significant challenges for practical use due to their tremendous computational costs in both development and deployment. In this work, we present a novel strategy, named the G2L framework, to increase the performance of large-scale foundation models, which consist of only $15\%$ of the parameters of giga-scale models, to a comparable performance level of giga-scale models in cancer-specific tasks. Our approach applies knowledge distillation, transferring the capabilities of a giga-scale model to a large-scale model, using just 1K pathology slides of a target cancer (e.g., breast, prostate, etc.). The resulting distilled model not only outperformed state-of-the-art models of the same size (i.e., large-scale) across several benchmarks but also, interestingly, surpassed the giga-scale teacher and huge-scale models in some benchmarks. In addition, the distilled model exhibited a higher robustness index, indicating improved resilience to image variations originating from multiple institutions. These findings suggest that the proposed distillation approach for a large-scale model is a data- and parameter-efficient way to achieve giga-scale-level performance for cancer-specific applications without prohibitive computational burden.
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            <a href="https://www.alphaxiv.org/abs/2510.11171v1" target="_blank" rel="noopener noreferrer">
                基于多视角流形证据融合的极化SAR图像分类
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            Multiview Manifold Evidential Fusion for PolSAR Image Classification
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Junfei Shi, Haojia Zhang, Haiyan Jin, Junhuai Li, Xiaogang Song, Yuanfan Guo, Ha...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于极化SAR图像的分类任务，属于遥感图像处理的特定领域应用。虽然涉及多视角融合和流形学习技术，但这些方法在遥感图像分析中的直接应用与推荐系统、搜索或广告的核心技术领域没有明显关联，也不涉及LLM、Transformer架构或异构数据建模的相关进展。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 09:05:51
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11171v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11171v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Polarimetric Synthetic Aperture Radar (PolSAR) covariance matrices and their extracted multi-features - such as scattering angle, entropy, texture, and boundary descriptors - provide complementary and physically interpretable information for image classification. Traditional fusion strategies typically concatenate these features or employ deep learning networks to combine them. However, the covariance matrices and multi-features, as two complementary views, lie on different manifolds with distinct geometric structures. Existing fusion methods also overlook the varying importance of different views and ignore uncertainty, often leading to unreliable predictions. To address these issues, we propose a Multiview Manifold Evidential Fusion (MMEFnet) method to effectively fuse these two views. It gives a new framework to integrate PolSAR manifold learning and evidence fusion into a unified architecture. Specifically, covariance matrices are represented on the Hermitian Positive Definite (HPD) manifold, while multi-features are modeled on the Grassmann manifold. Two different kernel metric learning networks are constructed to learn their manifold representations. Subsequently, a trusted multiview evidence fusion, replacing the conventional softmax classifier, estimates belief mass and quantifies the uncertainty of each view from the learned deep features. Finally, a Dempster-Shafer theory-based fusion strategy combines evidence, enabling a more reliable and interpretable classification. Extensive experiments on three real-world PolSAR datasets demonstrate that the proposed method consistently outperforms existing approaches in accuracy, robustness, and interpretability.
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            <a href="https://www.alphaxiv.org/abs/2510.11142v1" target="_blank" rel="noopener noreferrer">
                使用TUNEL原位杂交检测验证用于精子DNA碎片检测的人工智能工具
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            Validation of an Artificial Intelligence Tool for the Detection of Sperm DNA Fragmentation Using the TUNEL In Situ Hybridization Assay
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Byron Alexander Jacobs, Aqeel Morris, Ifthakaar Shaik, Frando Lin
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文涉及医学/生物学领域的精子DNA碎片检测应用，属于明确的无关主题。虽然提到了人工智能工具，但其应用场景完全在生殖医学领域，与推荐系统、搜索或广告没有任何关联。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 08:32:11
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11142v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11142v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Sperm DNA fragmentation (SDF) is a critical parameter in male fertility assessment that conventional semen analysis fails to evaluate. This study presents the validation of a novel artificial intelligence (AI) tool designed to detect SDF through digital analysis of phase contrast microscopy images, using the terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) assay as the gold standard reference. Utilising the established link between sperm morphology and DNA integrity, the present work proposes a morphology assisted ensemble AI model that combines image processing techniques with state-of-the-art transformer based machine learning models (GC-ViT) for the prediction of DNA fragmentation in sperm from phase contrast images. The ensemble model is benchmarked against a pure transformer `vision' model as well as a `morphology-only` model. Promising results show the proposed framework is able to achieve sensitivity of 60\% and specificity of 75\%. This non-destructive methodology represents a significant advancement in reproductive medicine by enabling real-time sperm selection based on DNA integrity for clinical diagnostic and therapeutic applications.
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            <a href="https://www.alphaxiv.org/abs/2510.11128v1" target="_blank" rel="noopener noreferrer">
                基于多层级跨模态知识迁移的热成像图像轻量化面部关键点检测
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            Lightweight Facial Landmark Detection in Thermal Images via Multi-Level Cross-Modal Knowledge Transfer
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Qiyi Tong, Olivia Nocentini, Marta Lagomarsino, Kuanqi Cai, Marta Lorenzini, Ara...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于热成像图像中的面部关键点检测，属于计算机视觉领域，与推荐系统、搜索或广告的核心技术没有直接关联。虽然涉及跨模态知识迁移，但其应用场景（热成像面部检测）在RecSys/Search/Ads领域缺乏明确的实用价值或应用潜力。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 08:19:56
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11128v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11128v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Facial Landmark Detection (FLD) in thermal imagery is critical for applications in challenging lighting conditions, but it is hampered by the lack of rich visual cues. Conventional cross-modal solutions, like feature fusion or image translation from RGB data, are often computationally expensive or introduce structural artifacts, limiting their practical deployment. To address this, we propose Multi-Level Cross-Modal Knowledge Distillation (MLCM-KD), a novel framework that decouples high-fidelity RGB-to-thermal knowledge transfer from model compression to create both accurate and efficient thermal FLD models. A central challenge during knowledge transfer is the profound modality gap between RGB and thermal data, where traditional unidirectional distillation fails to enforce semantic consistency across disparate feature spaces. To overcome this, we introduce Dual-Injected Knowledge Distillation (DIKD), a bidirectional mechanism designed specifically for this task. DIKD establishes a connection between modalities: it not only guides the thermal student with rich RGB features but also validates the student's learned representations by feeding them back into the frozen teacher's prediction head. This closed-loop supervision forces the student to learn modality-invariant features that are semantically aligned with the teacher, ensuring a robust and profound knowledge transfer. Experiments show that our approach sets a new state-of-the-art on public thermal FLD benchmarks, notably outperforming previous methods while drastically reducing computational overhead.
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            <a href="https://www.alphaxiv.org/abs/2510.11117v1" target="_blank" rel="noopener noreferrer">
                揭秘扩散模型中的数值理解能力——局限性与补救措施
            </a>
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            Demystifying Numerosity in Diffusion Models -- Limitations and Remedies
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yaqi Zhao, Xiaochen Wang, Li Dong, Wentao Zhang, Yuhui Yuan
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于扩散模型的数值理解能力，这属于纯粹的生成式AI领域，与推荐系统、搜索或广告的核心技术无关。扩散模型主要用于内容生成任务，而我的关注点在于排名、检索和用户行为建模等核心领域技术。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 08:07:24
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11117v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11117v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Numerosity remains a challenge for state-of-the-art text-to-image generation models like FLUX and GPT-4o, which often fail to accurately follow counting instructions in text prompts. In this paper, we aim to study a fundamental yet often overlooked question: Can diffusion models inherently generate the correct number of objects specified by a textual prompt simply by scaling up the dataset and model size? To enable rigorous and reproducible evaluation, we construct a clean synthetic numerosity benchmark comprising two complementary datasets: GrayCount250 for controlled scaling studies, and NaturalCount6 featuring complex naturalistic scenes. Second, we empirically show that the scaling hypothesis does not hold: larger models and datasets alone fail to improve counting accuracy on our benchmark. Our analysis identifies a key reason: diffusion models tend to rely heavily on the noise initialization rather than the explicit numerosity specified in the prompt. We observe that noise priors exhibit biases toward specific object counts. In addition, we propose an effective strategy for controlling numerosity by injecting count-aware layout information into the noise prior. Our method achieves significant gains, improving accuracy on GrayCount250 from 20.0\% to 85.3\% and on NaturalCount6 from 74.8\% to 86.3\%, demonstrating effective generalization across settings.
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            <a href="https://www.alphaxiv.org/abs/2510.11112v1" target="_blank" rel="noopener noreferrer">
                基于时空解耦与多尺度对齐的多模态疾病进展建模
            </a>
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            Multimodal Disease Progression Modeling via Spatiotemporal Disentanglement and Multiscale Alignment
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Chen Liu, Wenfang Yao, Kejing Yin, William K. Cheung, Jing Qin
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医疗领域的疾病进展建模，属于明确的无关主题。虽然提到了多模态和时空建模技术，但其核心应用场景是医疗诊断和疾病预测，与推荐系统、搜索或广告领域没有任何直接或间接的关联。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 08:02:36
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11112v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11112v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Longitudinal multimodal data, including electronic health records (EHR) and sequential chest X-rays (CXRs), is critical for modeling disease progression, yet remains underutilized due to two key challenges: (1) redundancy in consecutive CXR sequences, where static anatomical regions dominate over clinically-meaningful dynamics, and (2) temporal misalignment between sparse, irregular imaging and continuous EHR data. We introduce $\texttt{DiPro}$, a novel framework that addresses these challenges through region-aware disentanglement and multi-timescale alignment. First, we disentangle static (anatomy) and dynamic (pathology progression) features in sequential CXRs, prioritizing disease-relevant changes. Second, we hierarchically align these static and dynamic CXR features with asynchronous EHR data via local (pairwise interval-level) and global (full-sequence) synchronization to model coherent progression pathways. Extensive experiments on the MIMIC dataset demonstrate that $\texttt{DiPro}$ could effectively extract temporal clinical dynamics and achieve state-of-the-art performance on both disease progression identification and general ICU prediction tasks.
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            <a href="https://www.alphaxiv.org/abs/2510.11107v1" target="_blank" rel="noopener noreferrer">
                MoMaps：基于运动图的语义感知场景运动生成
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            MoMaps: Semantics-Aware Scene Motion Generation with Motion Maps
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jiahui Lei, Kyle Genova, George Kopanas, Noah Snavely, Leonidas Guibas
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉领域的场景运动生成，涉及运动图和语义感知技术，这与推荐系统、搜索或广告的核心技术领域没有直接关联。虽然标题提到'生成'，但这是纯粹的视觉内容生成应用，属于被明确排除的'Purely Vision'类别，没有明确的推荐/搜索/广告应用潜力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 07:56:19
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11107v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11107v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    This paper addresses the challenge of learning semantically and functionally meaningful 3D motion priors from real-world videos, in order to enable prediction of future 3D scene motion from a single input image. We propose a novel pixel-aligned Motion Map (MoMap) representation for 3D scene motion, which can be generated from existing generative image models to facilitate efficient and effective motion prediction. To learn meaningful distributions over motion, we create a large-scale database of MoMaps from over 50,000 real videos and train a diffusion model on these representations. Our motion generation not only synthesizes trajectories in 3D but also suggests a new pipeline for 2D video synthesis: first generate a MoMap, then warp an image accordingly and complete the warped point-based renderings. Experimental results demonstrate that our approach generates plausible and semantically consistent 3D scene motion.
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            <a href="https://www.alphaxiv.org/abs/2510.11092v1" target="_blank" rel="noopener noreferrer">
                未来感知的端到端驾驶：轨迹规划与场景演化的双向建模
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            Future-Aware End-to-End Driving: Bidirectional Modeling of Trajectory Planning and Scene Evolution
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Bozhou Zhang, Nan Song, Jingyu Li, Xiatian Zhu, Jiankang Deng, Li Zhang
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于自动驾驶领域，涉及轨迹规划和场景演化建模，与推荐系统、搜索或广告的核心技术领域完全无关。论文内容属于自动驾驶的特定应用场景，没有任何明显的技术可以迁移到RecSys/Search/Ads领域。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 07:41:47
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11092v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11092v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    End-to-end autonomous driving methods aim to directly map raw sensor inputs to future driving actions such as planned trajectories, bypassing traditional modular pipelines. While these approaches have shown promise, they often operate under a one-shot paradigm that relies heavily on the current scene context, potentially underestimating the importance of scene dynamics and their temporal evolution. This limitation restricts the model's ability to make informed and adaptive decisions in complex driving scenarios. We propose a new perspective: the future trajectory of an autonomous vehicle is closely intertwined with the evolving dynamics of its environment, and conversely, the vehicle's own future states can influence how the surrounding scene unfolds. Motivated by this bidirectional relationship, we introduce SeerDrive, a novel end-to-end framework that jointly models future scene evolution and trajectory planning in a closed-loop manner. Our method first predicts future bird's-eye view (BEV) representations to anticipate the dynamics of the surrounding scene, then leverages this foresight to generate future-context-aware trajectories. Two key components enable this: (1) future-aware planning, which injects predicted BEV features into the trajectory planner, and (2) iterative scene modeling and vehicle planning, which refines both future scene prediction and trajectory generation through collaborative optimization. Extensive experiments on the NAVSIM and nuScenes benchmarks show that SeerDrive significantly outperforms existing state-of-the-art methods.
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            <a href="https://www.alphaxiv.org/abs/2510.11091v1" target="_blank" rel="noopener noreferrer">
                CAD图纸中基于文本增强的全景符号识别
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            Text-Enhanced Panoptic Symbol Spotting in CAD Drawings
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xianlin Liu, Yan Gong, Bohao Li, Jiajing Huang, Bowen Du, Junchen Ye, Liyan Xu
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于CAD图纸中的符号识别和文本处理，属于计算机视觉和文档分析领域。与推荐系统、搜索或广告的核心技术进展没有直接关联，也不涉及LLM、Transformer架构或异构数据统一建模等关键技术。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 07:41:15
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11091v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11091v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    With the widespread adoption of Computer-Aided Design(CAD) drawings in engineering, architecture, and industrial design, the ability to accurately interpret and analyze these drawings has become increasingly critical. Among various subtasks, panoptic symbol spotting plays a vital role in enabling downstream applications such as CAD automation and design retrieval. Existing methods primarily focus on geometric primitives within the CAD drawings to address this task, but they face following major problems: they usually overlook the rich textual annotations present in CAD drawings and they lack explicit modeling of relationships among primitives, resulting in incomprehensive understanding of the holistic drawings. To fill this gap, we propose a panoptic symbol spotting framework that incorporates textual annotations. The framework constructs unified representations by jointly modeling geometric and textual primitives. Then, using visual features extract by pretrained CNN as the initial representations, a Transformer-based backbone is employed, enhanced with a type-aware attention mechanism to explicitly model the different types of spatial dependencies between various primitives. Extensive experiments on the real-world dataset demonstrate that the proposed method outperforms existing approaches on symbol spotting tasks involving textual annotations, and exhibits superior robustness when applied to complex CAD drawings.
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            <a href="https://www.alphaxiv.org/abs/2510.11073v1" target="_blank" rel="noopener noreferrer">
                ROFI：一种基于深度学习的眼科体征保留且可逆的患者面部匿名化方法
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            ROFI: A Deep Learning-Based Ophthalmic Sign-Preserving and Reversible Patient Face Anonymizer
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yuan Tian, Min Zhou, Yitong Chen, Fang Li, Lingzi Qi, Shuo Wang, Xieyang Xu, Yu ...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学领域（眼科）的面部匿名化技术，属于医疗图像处理和隐私保护范畴。这与我的核心关注领域（推荐系统、搜索、广告）以及所有相关技术方向完全无关，属于明确排除的医疗应用和隐私保护主题。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 07:12:23
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11073v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11073v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Patient face images provide a convenient mean for evaluating eye diseases, while also raising privacy concerns. Here, we introduce ROFI, a deep learning-based privacy protection framework for ophthalmology. Using weakly supervised learning and neural identity translation, ROFI anonymizes facial features while retaining disease features (over 98\% accuracy, $\kappa > 0.90$). It achieves 100\% diagnostic sensitivity and high agreement ($\kappa > 0.90$) across eleven eye diseases in three cohorts, anonymizing over 95\% of images. ROFI works with AI systems, maintaining original diagnoses ($\kappa > 0.80$), and supports secure image reversal (over 98\% similarity), enabling audits and long-term care. These results show ROFI's effectiveness of protecting patient privacy in the digital medicine era.
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            <a href="https://www.alphaxiv.org/abs/2510.11063v1" target="_blank" rel="noopener noreferrer">
                LSVOS 2025挑战赛报告：复杂视频对象分割的最新进展
            </a>
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            LSVOS 2025 Challenge Report: Recent Advances in Complex Video Object Segmentation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Chang Liu, Henghui Ding, Kaining Ying, Lingyi Hong, Ning Xu, Linjie Yang, Yuchen...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉领域的视频对象分割技术，属于纯粹的视觉研究方向。虽然视频分割在理论上可能与推荐系统中的内容理解相关，但论文标题明确指向视觉分割挑战赛报告，没有表明与推荐系统、搜索或广告的直接关联，也不涉及LLM、Transformer架构或异构数据建模等核心技术方向。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 07:02:09
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11063v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11063v1
                </a>
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    This report presents an overview of the 7th Large-scale Video Object Segmentation (LSVOS) Challenge held in conjunction with ICCV 2025. Besides the two traditional tracks of LSVOS that jointly target robustness in realistic video scenarios: Classic VOS (VOS), and Referring VOS (RVOS), the 2025 edition features a newly introduced track, Complex VOS (MOSEv2). Building upon prior insights, MOSEv2 substantially increases difficulty, introducing more challenging but realistic scenarios including denser small objects, frequent disappear/reappear events, severe occlusions, adverse weather and lighting, etc., pushing long-term consistency and generalization beyond curated benchmarks. The challenge retains standard ${J}$, $F$, and ${J\&F}$ metrics for VOS and RVOS, while MOSEv2 adopts ${J\&\dot{F}}$ as the primary ranking metric to better evaluate objects across scales and disappearance cases. We summarize datasets and protocols, highlight top-performing solutions, and distill emerging trends, such as the growing role of LLM/MLLM components and memory-aware propagation, aiming to chart future directions for resilient, language-aware video segmentation in the wild.
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            <a href="https://www.alphaxiv.org/abs/2510.11050v1" target="_blank" rel="noopener noreferrer">
                基于身份-属性解耦反演的零样本人脸编辑
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        <div class="mb-2 text-base text-gray-700">
            Zero-shot Face Editing via ID-Attribute Decoupled Inversion
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yang Hou, Minggu Wang, Jianjun Zhao
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">这篇论文专注于计算机视觉领域的人脸编辑技术，涉及人脸身份和属性的解耦表示。虽然标题提到零样本学习，但核心内容属于纯粹的视觉处理范畴，与推荐系统、搜索或广告的排名和建模需求没有直接关联。该技术缺乏在异构数据统一建模或推荐系统应用场景中的明显潜力。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 06:34:40
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11050v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11050v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Recent advancements in text-guided diffusion models have shown promise for general image editing via inversion techniques, but often struggle to maintain ID and structural consistency in real face editing tasks. To address this limitation, we propose a zero-shot face editing method based on ID-Attribute Decoupled Inversion. Specifically, we decompose the face representation into ID and attribute features, using them as joint conditions to guide both the inversion and the reverse diffusion processes. This allows independent control over ID and attributes, ensuring strong ID preservation and structural consistency while enabling precise facial attribute manipulation. Our method supports a wide range of complex multi-attribute face editing tasks using only text prompts, without requiring region-specific input, and operates at a speed comparable to DDIM inversion. Comprehensive experiments demonstrate its practicality and effectiveness.
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            <a href="https://www.alphaxiv.org/abs/2510.11047v1" target="_blank" rel="noopener noreferrer">
                基于LaryngealCT数据集对喉癌分期深度学习模型的基准测试
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            Benchmarking Deep Learning Models for Laryngeal Cancer Staging Using the LaryngealCT Dataset
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Nivea Roy, Son Tran, Atul Sajjanhar, K. Devaraja, Prakashini Koteshwara, Yong Xi...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学领域（喉癌分期）的深度学习应用，属于明确的医疗领域特定应用。虽然涉及深度学习模型基准测试，但内容与推荐系统、搜索、广告或相关使能技术完全无关，属于明确的无关主题范畴。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 06:25:19
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11047v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11047v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Laryngeal cancer imaging research lacks standardised datasets to enable reproducible deep learning (DL) model development. We present LaryngealCT, a curated benchmark of 1,029 computed tomography (CT) scans aggregated from six collections from The Cancer Imaging Archive (TCIA). Uniform 1 mm isotropic volumes of interest encompassing the larynx were extracted using a weakly supervised parameter search framework validated by clinical experts. 3D DL architectures (3D CNN, ResNet18,50,101, DenseNet121) were benchmarked on (i) early (Tis,T1,T2) vs. advanced (T3,T4) and (ii) T4 vs. non-T4 classification tasks. 3D CNN (AUC-0.881, F1-macro-0.821) and ResNet18 (AUC-0.892, F1-macro-0.646) respectively outperformed the other models in the two tasks. Model explainability assessed using 3D GradCAMs with thyroid cartilage overlays revealed greater peri-cartilage attention in non-T4 cases and focal activations in T4 predictions. Through open-source data, pretrained models, and integrated explainability tools, LaryngealCT offers a reproducible foundation for AI-driven research to support clinical decisions in laryngeal oncology.
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            <a href="https://www.alphaxiv.org/abs/2510.11026v1" target="_blank" rel="noopener noreferrer">
                GIR-Bench：用于生成具有推理能力的图像的多功能基准
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            GIR-Bench: Versatile Benchmark for Generating Images with Reasoning
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Hongxiang Li, Yaowei Li, Bin Lin, Yuwei Niu, Yuhang Yang, Xiaoshuang Huang, Jiay...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于图像生成基准测试，属于纯粹的视觉生成领域，与推荐系统、搜索或广告的核心技术无关。虽然标题提到'推理'，但这是针对图像生成的推理能力评估，没有明确的潜在应用可以转化为RecSys/Search/Ads领域的实际技术。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 05:50:44
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11026v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11026v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Unified multimodal models integrate the reasoning capacity of large language models with both image understanding and generation, showing great promise for advanced multimodal intelligence. However, the community still lacks a rigorous reasoning-centric benchmark to systematically evaluate the alignment between understanding and generation, and their generalization potential in complex visual tasks. To this end, we introduce \textbf{GIR-Bench}, a comprehensive benchmark that evaluates unified models across three complementary perspectives. Firstly, we investigate understanding-generation consistency (GIR-Bench-UGC), asking whether models can consistently leverage the same knowledge in both understanding and generation tasks. Secondly, we investigate whether models can perform reasoning-centric text-to-image generation that requires applying logical constraints and implicit knowledge to generate faithful visual content (GIR-Bench-T2I). Thirdly, we evaluate whether models can handle multi-step reasoning in editing (GIR-Bench-Edit). For each subset, we carefully design different task-specific evaluation pipelines tailored for each task. This enables fine-grained and interpretable evaluation while mitigating biases from the prevalent MLLM-as-a-Judge paradigm. Extensive ablations over various unified models and generation-only systems have shown that: Although unified models are more capable of reasoning-driven visual tasks, they still exhibit a persistent gap between understanding and generation. The data and code for GIR-Bench are available at \href{https://hkust-longgroup.github.io/GIR-Bench}{https://hkust-longgroup.github.io/GIR-Bench}.
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            <a href="https://www.alphaxiv.org/abs/2510.11005v1" target="_blank" rel="noopener noreferrer">
                频域为腹部医学图像分割解锁新视角
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        <div class="mb-2 text-base text-gray-700">
            Frequency Domain Unlocks New Perspectives for Abdominal Medical Image Segmentation
        </div>
        
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Kai Han, Siqi Ma, Chengxuan Qian, Jun Chen, Chongwen Lyu, Yuqing Song, Zhe Liu
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学图像分割这一特定领域应用，属于明确的医学领域研究。论文内容涉及腹部医学图像处理，与推荐系统、搜索或广告领域没有任何技术关联或应用潜力。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 04:44:43
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.11005v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.11005v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Accurate segmentation of tumors and adjacent normal tissues in medical images is essential for surgical planning and tumor staging. Although foundation models generally perform well in segmentation tasks, they often struggle to focus on foreground areas in complex, low-contrast backgrounds, where some malignant tumors closely resemble normal organs, complicating contextual differentiation. To address these challenges, we propose the Foreground-Aware Spectrum Segmentation (FASS) framework. First, we introduce a foreground-aware module to amplify the distinction between background and the entire volume space, allowing the model to concentrate more effectively on target areas. Next, a feature-level frequency enhancement module, based on wavelet transform, extracts discriminative high-frequency features to enhance boundary recognition and detail perception. Eventually, we introduce an edge constraint module to preserve geometric continuity in segmentation boundaries. Extensive experiments on multiple medical datasets demonstrate superior performance across all metrics, validating the effectiveness of our framework, particularly in robustness under complex conditions and fine structure recognition. Our framework significantly enhances segmentation of low-contrast images, paving the way for applications in more diverse and complex medical imaging scenarios.
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            <a href="https://www.alphaxiv.org/abs/2510.10993v1" target="_blank" rel="noopener noreferrer">
                基于多视角一致性的透视感知3D高斯修复
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            Perspective-aware 3D Gaussian Inpainting with Multi-view Consistency
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yuxin Cheng, Binxiao Huang, Taiqiang Wu, Wenyong Zhou, Chenchen Ding, Zhengwu Li...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于3D视觉和图形学中的高斯修复技术，属于纯粹的计算机视觉领域。虽然提到了多视角一致性，但这主要针对3D场景重建和修复，与推荐系统、搜索或广告中的排序、用户建模、内容理解等核心问题没有直接关联。该技术缺乏在RecSys/Search/Ads领域的明确应用场景。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 04:10:39
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.10993v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.10993v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
                     查看完整摘要 <i class="fa fa-chevron-down ml-1 text-xs"></i> 
                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    3D Gaussian inpainting, a critical technique for numerous applications in virtual reality and multimedia, has made significant progress with pretrained diffusion models. However, ensuring multi-view consistency, an essential requirement for high-quality inpainting, remains a key challenge. In this work, we present PAInpainter, a novel approach designed to advance 3D Gaussian inpainting by leveraging perspective-aware content propagation and consistency verification across multi-view inpainted images. Our method iteratively refines inpainting and optimizes the 3D Gaussian representation with multiple views adaptively sampled from a perspective graph. By propagating inpainted images as prior information and verifying consistency across neighboring views, PAInpainter substantially enhances global consistency and texture fidelity in restored 3D scenes. Extensive experiments demonstrate the superiority of PAInpainter over existing methods. Our approach achieves superior 3D inpainting quality, with PSNR scores of 26.03 dB and 29.51 dB on the SPIn-NeRF and NeRFiller datasets, respectively, highlighting its effectiveness and generalization capability.
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            </details>
    </div>
</div><!--
 * @Author: Doragd doragd@users.noreply.github.com
 * @Date: 2025-10-09 23:23:38
 * @LastEditors: Doragd doragd@users.noreply.github.com
 * @LastEditTime: 2025-10-10 00:41:41
 * @FilePath: /Algorithm-Practice-in-Industry/paperBotV2/frontend/templates/normal_paper_template.html
 * @Description: 这是默认设置,请设置`customMade`, 打开koroFileHeader查看配置 进行设置: https://github.com/OBKoro1/koro1FileHeader/wiki/%E9%85%8D%E7%BD%AE
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            <a href="https://www.alphaxiv.org/abs/2510.10933v1" target="_blank" rel="noopener noreferrer">
                DKPMV：基于多视角RGB帧的密集关键点融合用于无纹理物体的6D姿态估计
            </a>
        </h3>
        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>1/10
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            DKPMV: Dense Keypoints Fusion from Multi-View RGB Frames for 6D Pose Estimation of Textureless Objects
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jiahong Chen, Jinghao Wang, Zi Wang, Ziwen Wang, Banglei Guan, Qifeng Yu
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉中的6D姿态估计，特别是针对无纹理物体的多视角RGB帧处理。这与推荐系统、搜索或广告的核心领域没有直接关联，也不涉及LLM技术、Transformer架构或异构数据统一建模。该技术主要应用于机器人、增强现实等视觉领域，与我的关注焦点无关。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-13 02:45:55
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.10933v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.10933v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.RO</span></div>
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                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    6D pose estimation of textureless objects is valuable for industrial robotic applications, yet remains challenging due to the frequent loss of depth information. Current multi-view methods either rely on depth data or insufficiently exploit multi-view geometric cues, limiting their performance. In this paper, we propose DKPMV, a pipeline that achieves dense keypoint-level fusion using only multi-view RGB images as input. We design a three-stage progressive pose optimization strategy that leverages dense multi-view keypoint geometry information. To enable effective dense keypoint fusion, we enhance the keypoint network with attentional aggregation and symmetry-aware training, improving prediction accuracy and resolving ambiguities on symmetric objects. Extensive experiments on the ROBI dataset demonstrate that DKPMV outperforms state-of-the-art multi-view RGB approaches and even surpasses the RGB-D methods in the majority of cases. The code will be available soon.
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